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Farhadi, Mahdi. "A self organizing map (SOM) based electric load classification." Facta universitatis - series: Electronics and Energetics 31, no. 4 (2018): 571–83. http://dx.doi.org/10.2298/fuee1804571f.
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It is of vital importance to use proper training data to perform accurate shortterm load forecasting (STLF) based on artificial neural networks. The pattern of the loads which are used for the training of Kohonen Self Organizing Map (SOM) neural network in STLF models should be of the highest similarity with the pattern of the electric load of the forecasting day. In this paper, an electric load classifier model is proposed which relies on the pattern recognition capability of SOM. The performance of the proposed electric load classifier method is evaluated by Iran electric grid data. The proposed method requires a very few number of training samples for training the Kohonen neural network of the STLF model and can accurately predict electric load in the network.
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Ariana, Anak Agung Gede Bagus, I. Ketut Gede Darma Putra, and Linawati Linawati. "Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail." Majalah Ilmiah Teknologi Elektro 16, no. 2 (2017): 55. http://dx.doi.org/10.24843/mite.2017.v16i02p10.
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Abstract— This study investigates the performance of artificial neural network method on clustering method. Using UD. Fenny’s customer profile in year 2009 data set with the Recency, Frequency and Monetary model data. Clustering methods were compared in this study is between the Self Organizing Map and Adaptive Resonance Theory 2. The performance evaluation method validation is measured by the index cluster validation. Validation index clusters are used, among others, Davies-Bouldin index, index and index Dunn Silhouette. The test results show the method Self Organizing Map is better to process the data clustering. Index term— Data Mining, Artificial Neural Network, Self Organizing Map, Adaptive Resonance Theory 2. Intisari—Penelitian ini ingin mengetahui unjuk kerja metode clustering data berbasis jaringan saraf tiruan. Menggunakan data set profil pelanggan UD. Fenny tahun 2009 dengan atribut Recency, Frequency dan Monetary. Metode clustering yang dibandingkan pada penelitian ini adalah Self Organizing Map dan Adaptive Resonance Theory 2. Evaluasi kinerja metode dilakukan dengan mengukur validasi index dari cluster yang terbentuk. Validasi cluster yang digunakan antara lain Indeks Davies-Bouldin, Indeks Dunn dan Indeks Silhouette. Hasil pengujian menunjukkan metode Self Organizing Map lebih baik dalam melakukan proses clustering data. Kata Kunci— Data Mining, Jaringan Saraf Tiruan Self Organizing Map, Adaptive Resonance Theory 2.
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Khotimah, Tutik, and Darsin Darsin. "CLUSTERING PERKEMBANGAN KASUS COVID-19 DI INDONESIA MENGGUNAKAN SELF ORGANIZING MAP." Jurnal Dialektika Informatika (Detika) 1, no. 1 (2020): 23–26. http://dx.doi.org/10.24176/detika.v1i1.5596.
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Tujuan dari penelitian ini adalah melakukan pengelompokan daerah-daerah di Indonesia berdasarkan perkembangan kasus Covid-19. Pada penelitian ini digunakan Jaringan Syaraf Tiruan Kohonen yang disebut juga Self Organizing Map (SOM). Data yang digunakan adalah data situasi terkini penyebaran Covid-19 di Indonesia per tanggal 19 September 2020. Data ini diperoleh dari Kementerian Kesehatan Republik Indonesia.
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Pasa, Leandro Antonio, José Alfredo F. Costa, and Marcial Guerra de Medeiros. "An ensemble algorithm for Kohonen self-organizing map with different sizes." Logic Journal of the IGPL 25, no. 6 (2017): 1020–33. http://dx.doi.org/10.1093/jigpal/jzx046.
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Abstract Data Clustering aims to discover groups within the data based on similarities, with a minimal, if any, knowledge of their structure. Variations in the results may occur due to many factors, including algorithm parameters, initialization and stopping criteria. The usage of different attributes or even different subsets of data usually lead to different results. Self-organizing maps (SOM) has been widely used for a variety of tasks regarding data analysis, including data visualization and clustering. A machine committee, or ensemble, is a set of neural networks working independently with some system that enable the combination of individual results into a single output, with the aim to achieve a better generalization compared to a unique neural network. This article presents a new ensemble method that uses SOM networks. Cluster validity indexes are used to combine neuron weights from different maps with different sizes. Results are shown from simulations with real and synthetic data, from the UCI Repository and Fundamental Clustering Problems Suite. The proposed method presented promising results, with increased performance compared with conventional single Kohonen map.
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Suwirmayanti, Ni Luh Gede Pivin. "Penerapan Teknik Clustering Untuk Pengelompokkan Konsentrasi Mahasiswa Dengan Metode Self Organizing Map." Jurnal Ilmiah Intech : Information Technology Journal of UMUS 2, no. 01 (2020): 11–20. http://dx.doi.org/10.46772/intech.v2i01.182.
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Prodi sistem Komputer memiliki beberapa konsentrasi atau peminatan ketika mahasiswa menginjak semester pertengahan yaitu semester 5. Penentuan konsetrasi ini sangatlah riskan untuk mahasiswa, karena mahasiswa harus memilih sesuai dengan bakat yang ditunjang oleh nilai dari matakuliah pendukung konsetrasi tersebut. Standar dalam menentukan konsentrasi bagi mahasiswa dapat dipengaruhi oleh beberapa faktor, antara lain nilai akdemikyang ditunjukkan dengan nilai matakuliah mahasiswa serta IPK dari mahasiswa tersebut. Penelitian ini bertujuan mengimplementasikan algoritma Self Organizing Map (SOM) untuk clustering konsetrasi mahasiswa. Melihat dari permasalahan utama dari sistem clustering adalah bagaimana membagi sekelompok data yang memiliki kesamaan semirip mungkin ke dalam satu cluster. Metode Self Organizing Map (SOM) pertama kali diperkenalkan pada tahun1981 oleh Prof. Teuvo Kohonen, algoritma ini melakukan proses clustering dengan membentuk jaringan kohonen yang digunakan untuk mengelompokkan data berdasarkan karakteristik serta fitur-fitur dari datanya. Yang dijadikan acuan dasara atau Parameter untuk metode clustering ini adalah nilai-nilai matakuliah di semester sebelumnya, dimana matakuliah tersebut merupakan matakuliah prasyarat . Pengujian dilakukan dengan menguji fungsionalitas sistem, dan mengevaluasi cluster yang dihasilkannya. Hasil nilai cluster yang terbentuk dapat dijadikan sebagai acuan informasi penentu kelompok konsentrasi bagi mahasiswa prodi Sistem Komputer.
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Setyanngsih, Fatma Agus. "IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 4, no. 2 (2017): 198. http://dx.doi.org/10.20527/klik.v4i2.105.
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<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>
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Kapita, Syarifuddin, and Saiful Abdullah. "APLIKASI JARINGAN SYARAF TIRUAN KOHONEN SELF ORGANIZING MAP (K-SOM) PADA DATA MUTU SEKOLAH." JIKO (Jurnal Informatika dan Komputer) 3, no. 1 (2020): 56–61. http://dx.doi.org/10.33387/jiko.v3i1.1737.
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Kolasa, Marta, Rafał Długosz, Wojciech Jóźwicki, Jolanta Pauk, Aleksandra Świetlicka, and Pierre André Farine. "Analysis of Significant Prognostic Factors of Patients with Bladder Cancer Using Self-Organizing Maps." Solid State Phenomena 199 (March 2013): 223–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.199.223.
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This study presents a new approach to determine significant prognostic factors for patients suffering from the bladder cancer. The analysis of medical data has been performed by the use of the Kohonen self-organizing map (SOM). The SOM allows visualizing and identifying the prognostic factors indicating which of them are significant. A database comprised of ninety patients has been used in this study. Seven predictors were investigated. The cluster analysis indicates that the significant prognostic factors for the bladder cancer are: histological grade (cG) and stage (cT). The obtained results also showed that the sex and the cG variables are highly correlated and that the number of non-classic differentiation (NDNc) features in bladder cancer is somewhat correlated to surgically removed lymphnode number (LN) and metastatic positive lymphnode number (PLN).
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Schreck, Tobias, Jürgen Bernard, Tatiana von Landesberger, and Jörn Kohlhammer. "Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps." Information Visualization 8, no. 1 (2009): 14–29. http://dx.doi.org/10.1057/ivs.2008.29.
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Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.
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Galvan, Diego, Luciane Effting, Hágata Cremasco, and Carlos Adam Conte-Junior. "The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks." Medicina 57, no. 3 (2021): 235. http://dx.doi.org/10.3390/medicina57030235.
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Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.
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Schulz, Reiner, and James A. Reggia. "Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps." Neural Computation 16, no. 3 (2004): 535–61. http://dx.doi.org/10.1162/089976604772744901.
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We examine the extent to which modified Kohonen self-organizing maps (SOMs) can learn unique representations of temporal sequences while still supporting map formation. Two biologically inspired extensions are made to traditional SOMs: selection of multiple simultaneous rather than single “winners” and the use of local intramap connections that are trained according to a temporally asymmetric Hebbian learning rule. The extended SOM is then trained with variable-length temporal sequences that are composed of phoneme feature vectors, with each sequence corresponding to the phonetic transcription of a noun. The model transforms each input sequence into a spatial representation (final activation pattern on the map). Training improves this transformation by, for example, increasing the uniqueness of the spatial representations of distinct sequences, while still retaining map formation based on input patterns. The closeness of the spatial representations of two sequences is found to correlate significantly with the sequences' similarity. The extended model presented here raises the possibility that SOMs may ultimately prove useful as visualization tools for temporal sequences and as preprocessors for sequence pattern recognition systems.
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Kuremoto, Takashi, Takahito Komoto, Kunikazu Kobayashi, and Masanao Obayashi. "Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System." Journal of Robotics 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/307293.
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An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system.
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Oyana, Tonny J., Luke E. K. Achenie, and Joon Heo. "The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data." Computational and Mathematical Methods in Medicine 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/683265.
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The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen’s SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen’s SOM.
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Kolasa, Marta, Rafał Długosz, and Krzysztof Bieliński. "Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level." International Journal of Electronics and Telecommunications 56, no. 4 (2010): 367–73. http://dx.doi.org/10.2478/v10177-010-0048-6.
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Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor LevelA new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18μm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolution has a dominant influence on the overall circuit complexity i.e. the chip area and the energy consumption. The proposed neighborhood mechanism is very fast. For an example neighborhood range of 15 a delay between the first and the last neighboring neuron does not exceed 20 ns. This in practice means that the adaptation process starts in all neighboring neurons almost at the same time. As a result, data rates of 10-20 MHz are achievable, independently on the number of neurons in the map. The proposed SOM dissipates the power in-between 100 mW and 1 W, depending on the number of neurons in the map. For the comparison, the same network realized on PC achieves in simulations data rates in-between 10 Hz and 1 kHz. Data rate is in this case linearly dependend on the number of neurons.
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Thurn, Nicholas, Mary Williams, and Michael Sigman. "Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples." Separations 5, no. 4 (2018): 52. http://dx.doi.org/10.3390/separations5040052.
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Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, ASTM E1618-14. This standard method does not separately address the identification of substrate pyrolysis patterns. This report details the use of a Kohonen self-organizing map coupled with extracted ion spectra to organize ignitable liquids and substrate pyrolysis samples on a two-dimensional map with groupings that correspond to the ASTM-classifications and separate the substrate pyrolysis samples from the ignitable liquids. The component planes give important information regarding the ions from the extracted ion spectra that contribute to the different classes. Some additional insight is gained into grouping of substrate pyrolysis samples based on the nature of the unburned material as a wood or non-wood material. Further subclassification was not apparent from the self-organizing maps (SOM) results.
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Moonlight, Lady Silk. "SISTEM PENGENALAN WAJAH BERBASIS JARINGAN SYARAF TIRUAN SELF ORGANIZINGMAP (SOM) DENGAN PEMROSESAN AWAL DISCRETE COSINE TRANSFORM (DCT)." Jurnal Penelitian 4, no. 3 (2019): 29–39. http://dx.doi.org/10.46491/jp.v4e3.372.29-39.
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Citra wajah merupakan salah satu fitur biometrik yang dapat dijadikan sebagai bukti autentik dari seseorang. Sistem pengenalan wajah (Face Recognition) secara komputerisasi, akan mengetahui identitas diri seseorang. Dalam proses pelatihan citra wajah, penggunaan piksel dari citra wajah secara langsung dapat mengakibatkan banyaknya fitur-fitur wajah yang tidak dapat terekstraksi dengan baik. Maka dari itu diperlukan suatu pemrosesan awal yang dapat mengekstraksi fitur-fitur wajah dengan baik. Dimana pada penelitian ini digunakan Discrete Cosine Transform (DCT) sebagai pemrosesan awal dan Penggunaan Jaringan Syaraf Tiruan Self Organizing Map (SOM)/Kohonen dalam proses pelatihannya. Dengan menggunakan DCT jaringan akan cepat belajar dan dapat mengenali citra dengan kesalahan yang minimum sehingga didapatkan sistem yang dapat bekerja cukup baik dan efisien.
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Costea, Adrian. "On building early-warning systems for preventing the deterioration of financial institutions’ performance." Proceedings of the International Conference on Applied Statistics 1, no. 1 (2019): 194–202. http://dx.doi.org/10.2478/icas-2019-0017.
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Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.
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de Matos, Marcílio Castro, Paulo Léo Osorio, and Paulo Roberto Johann. "Unsupervised seismic facies analysis using wavelet transform and self-organizing maps." GEOPHYSICS 72, no. 1 (2007): P9—P21. http://dx.doi.org/10.1190/1.2392789.
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Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.
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Bondarenko, Andrey, and Arkady Borisov. "Research of Artificial Neural Networks Abilities in Printed Words Recognition." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 124–29. http://dx.doi.org/10.2478/v10143-010-0053-3.
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Research of Artificial Neural Networks Abilities in Printed Words Recognition This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks, namely feedforward back-propagation multilayer perceptron (MLP) and Kohonen self-organizing features map (SOM).
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Barletta, Vita Santa, Danilo Caivano, Antonella Nannavecchia, and Michele Scalera. "Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach." Future Internet 12, no. 7 (2020): 119. http://dx.doi.org/10.3390/fi12070119.
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The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.
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Deetz, Marcus. "K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 5, no. 3 (2019): 43–57. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.53.1006.
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With the implementation of the 2-step approach according to Vesanto & Alhoniemi (2000), this article extends the procedure of visual evaluation of the Kohonen Maps usually chosen in the hedge fund literature for classification with Self-Organizing Maps. It introduces an automated procedure which guarantees a consistent combination of adjacent output units and thus an objective classification. The practical application of this method results in a reduction of the strategy groups specified by the database. This is also accompanied by a significant reduction in the Davies Bouldin Index (DBI) of the SOM partitions. Since a small dispersion within the clusters and large distances between the clusters lead to small DBIs, a minimization of this measure is desired. This significantly better partitioning of SOMs in comparison to the classification of hedge funds into the categorization scheme specified by the database provider can be observed in all examined data samples (robustness analyses). Ultimately, none of the original 23 strategy groups can be empirically validated. Furthermore, no stable classification can be found. Both the number of empirically determined categories (SOM clusters) and the composition of these clusters differ significantly in the subsamples examined. Thus the results essentially confirm the results and conclusions in the literature, according to which the original, self-classified strategy labels of the database providers are misleading and therefore do not contain any information content.
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Bishop, Christopher M., Markus Svensén, and Christopher K. I. Williams. "GTM: The Generative Topographic Mapping." Neural Computation 10, no. 1 (1998): 215–34. http://dx.doi.org/10.1162/089976698300017953.
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Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis, which is based on a linear transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (1982) and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multiphase oil pipeline.
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Barletta, Vita Santa, Danilo Caivano, Antonella Nannavecchia, and Michele Scalera. "A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks." Applied Sciences 10, no. 15 (2020): 5062. http://dx.doi.org/10.3390/app10155062.
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The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X–Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF–K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages.
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Várbíró, Gábor, Gábor Borics, Keve T. Kiss, Katalin E. Szabó, Andelka Plenković-Moraj, and Éva Ács. "Use of Kohonen Self Organizing Maps (SOM) for the characterization of benthic diatom associations of the River Danube and its tributaries." River Systems 17, no. 3-4 (2007): 395–403. http://dx.doi.org/10.1127/lr/17/2007/395.
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Pham, D. T., M. S. Packianather, and E. Y. A. Charles. "Control chart pattern clustering using a new self-organizing spiking neural network." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 10 (2008): 1201–11. http://dx.doi.org/10.1243/09544054jem1054.
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This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.
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Ovechkin, Maksim V., Eugeniy S. Shelihov, and Julia I. Ovechkina. "The analysis of methods effectiveness of automated non-destructive testing of products based on Data Mining methods." MATEC Web of Conferences 224 (2018): 02062. http://dx.doi.org/10.1051/matecconf/201822402062.
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Purpose of the study: the analysis of the effectiveness of automated nondestructive testing methods within the objectives of data clustering on the use of short-wave electromagnetic radiation in flaw detection. Research methods: Kohonen self-organizing maps (SOM). The relevance of the work is that due to the increased demand for quality and reliability of products are becoming increasingly important physical methods for automated control of metals and products thereof that do not require cutting or fracture specimens of finished productes. The article noted common features of methods of short-wave electromagnetic control of products. The effectiveness of the Data Mining approach to the construction of a hypothesis on the interrelationships of data groups on non-destructive testing of products is substantiated. As an instrument, the method of self-organizing Kohonen maps was chosen. An example of a part of training data and neural network configuration parameters performing the task of visualization and clustering is given. It is concluded about the lead electromagnetic methods of automated control of complex products in production. The resulting distance matrix and the cluster map are shown. An example of applying the results of analysis to the problem of testing spot welded joints is considered. Given the further directions of research is to develop a computer image processing techniques in the framework of automated non-destructive testing systems.
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Bonifácio Oliveira Cardoso, Daniel, Luiza Amaral Medeiros, Gabriela de Oliveira Carvalho, et al. "Use of computational intelligence in the genetic divergence of colored cotton plants." Bioscience Journal 37 (January 20, 2021): e37007. http://dx.doi.org/10.14393/bj-v37n0a2021-53634.
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The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes.
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Toiviainen, Petri, and Carol L. Krumhansl. "Measuring and Modeling Real-Time Responses to Music: The Dynamics of Tonality Induction." Perception 32, no. 6 (2003): 741–66. http://dx.doi.org/10.1068/p3312.
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We examined a variety of real-time responses evoked by a single piece of music, the organ Duetto BWV 805 by J S Bach. The primary data came from a concurrent probe-tone method in which the probe-tone is sounded continuously with the music. Listeners judged how well the probe tone fit with the music at each point in time. The process was repeated for all probe tones of the chromatic scale. A self-organizing map (SOM) [Kohonen 1997 Self-organizing Maps (Berlin: Springer)] was used to represent the developing and changing sense of key reflected in these judgments. The SOM was trained on the probe-tone profiles for 24 major and minor keys (Krumhansl and Kessler 1982 Psychological Review89 334–368). Projecting the concurrent probe-tone data onto the map showed changes both in the perceived keys and in their strengths. Two dynamic models of tonality induction were tested. Model 1 is based on pitch class distributions. Model 2 is based on the tone-transition distributions; it tested the idea that the order of tones might provide additional information about tonality. Both models contained dynamic components for characterizing pitch strength and creating pitch memory representations. Both models produced results closely matching those of the concurrent probe-tone data. Finally realtime judgments of tension were measured. Tension correlated with distance away from the predominant key in the direction of keys built on the dominant and supertonic tones, and also correlated with dissonance.
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En-Naimani, Zakariae, Mohamed Lazaar, and Mohamed Ettaouil. "Architecture Optimization Model for the Probabilistic Self-Organizing Maps and Speech Compression." International Journal of Computational Intelligence and Applications 15, no. 02 (2016): 1650007. http://dx.doi.org/10.1142/s1469026816500073.
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The probabilistic self-organizing map (PRSOM) is an improved version of the Kohonen classical model (SOM) that appeared in the late 1990’s. In the last years, the interest of probabilistic methods, especially in the fields of clustering and classification has increased, and the PRSOM has been successfully employed in many technological uses, such as: pattern recognition, speech recognition, data compression, medical diagnosis, etc. Mathematically, the PRSOM gives an estimation of the density probability function of a set of samples. And this estimation depends on the parameters given by the architecture of the model. Therefore, the main problem of this model, that we try to approach in this paper, is the architecture choice (the number of neurons and the initialization parameters). In summary, in the present paper, we describe a recent approach of PRSOM trying to find a solution to the problem below. For that, we propose an architecture optimization model that is a mixed integer nonlinear optimization model under linear constraints, resolved by the genetic algorithm. Then to prove the efficiency of the proposed model, we chose to apply it on a speech compression technique based on the determination of the optimal codebook, and finally, we give an implementation and an evaluation of the proposed method that we compare with the results of the classical model.
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Thomas, Elizabeth, Marc M. Van Hulle, and Rufin Vogel. "Encoding of Categories by Noncategory-Specific Neurons in the Inferior Temporal Cortex." Journal of Cognitive Neuroscience 13, no. 2 (2001): 190–200. http://dx.doi.org/10.1162/089892901564252.
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In order to understand how the brain codes natural categories, e.g., trees and fish, recordings were made in the anterior part of the macaque inferior temporal (IT) cortex while the animal was performing a tree/nontree categorization task. Most single cells responded to exemplars of more than one category while other neurons responded only to a restricted set of exemplars of a given category. Since it is still not known which type of cells contribute and what is the nature of the code used for categorization in IT, we have performed an analysis on single-cell data. A Kohonen self-organizing map (SOM), which uses an unsupervised (competitive) learning algorithm, was used to study the single cell responses to tree and nontree images. Results from the Kohonen SOM indicated that the collected neuronal data consisting of spike counts was sufficient to account for a good level of categorization success (approximately 83%) when categorizing a group of 200 trees and nontrees. Contrary to intuition, the results of the investigation suggest that the population of category-specific neurons (neurons that respond only to trees or only to nontrees) was unimportant to the categorization. Instead, a large majority of the neurons that were most important to the categorization was found to belong to a class of more broadly tuned cells, namely, cells that responded to both categories but that favored one category over the other by seven or more images. A simple algebraic operation (without the Kohonen SOM) between the above-mentioned noncategory-specific neurons confirmed the contribution of these neurons to categorization. Thus, the modeling results suggest (1) that broadly tuned neurons are critical for categorization, and (2) that only one additional layer of processing is required to extract the categories from a population of IT neurons.
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Soldic-Aleksic, Jasna, and Rade Stankic. "A comparative analysis of Serbia and the EU member states in the context of networked readiness index values." Ekonomski anali 60, no. 206 (2015): 45–86. http://dx.doi.org/10.2298/eka1506045s.
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Nowadays it is generally accepted that information and communication technologies (ICT) are important drivers and ?enabling? technologies that have a broad impact on many sectors of the economy and social life. Therefore, measuring the level of ICT development, their economic and social impact, and the country?s readiness to use them are of great importance. In this paper we present the conceptual framework of the Networked Readiness Index (NRI) proposed by the World Economic Forum, and analyse the relative position of Serbia and its ?distance? from the EU member states in the domain of NRI indicator variables. For this purpose we have applied the Kohonen Self-Organizing Map (the SOM algorithm), which provides the visual image, as a virtual map, of observed countries and their groupings. The resulting SOM map indicates that in the complex NRI space, Serbia is located in a group of EU states that includes Romania, Croatia, Bulgaria, Cyprus, Greece, Italy, Poland, the Czech Republic, and the Slovak Republic. In comparison to other countries, this group shows the poorest performance in the NRI landscape. In addition, our empirical analysis points to the areas in which policy intervention can boost the impact of ICT on Serbian economic development and growth.
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Tachibana, Kanta, and Takeshi Furuhashi. "Self-Organizing Map with Generating and Moving Neurons in Visible Space." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (2007): 626–32. http://dx.doi.org/10.20965/jaciii.2007.p0626.
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Kohonen’s Self-Organizing feature Map (SOM) is used to obtain topology-preserving mapping from high-dimensional feature space to visible space of two or fewer dimensions. The SOM algorithm uses a fixed structure of neurons in visible space and learns a dataset by updating reference points in feature space. The mapping result depends on mapping parameters fixed, which are the number and visible positions of neurons, and parameters of learning, which are the learning rate, total iteration, and the setting of neighboring radii. To obtain a satisfactory result, the user usually must try many combinations of parameters. It is wasteful, however, to set up every possible combination of parameters and to repeatedly run the algorithm from the beginning because the computation cost for learning is large, especially for a large-scale dataset. These problems arise due to the fixing of two types of mapping parameters, i.e., the number and visible positions of neurons. The high computation cost is mainly in the calculation of distances from each sample to all reference points. At the beginning of learning, reference points should be adjusted globally to preserve the topology well because they are initially set far from optimal positions in feature space, e.g. randomly. Such many reference points subdivides feature space into unnecessarily fine Voronoi regions. To avoid this computational waste, it is natural to start learning with a small number of neurons and increase the number of neurons during learning. We propose a new SOM method that varies the number and visible positions of neurons, and thus is applicable also to visible torus and sphere spaces. We apply our proposal to spherical visible space. We use central Voronoi tessellation to move visible positions for two reasons: to tessellate visible space evenly for easy visualization and to level the number of neighboring neurons and better preserve topology. We demonstrate the effect of generating neurons to reduce computation cost and of moving visible positions in visualization and topology preservation.
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Curry, Bruce, Fiona Davies, Martin Evans, Luiz Moutinho, and Paul Phillips. "The Kohonen Self-organising Map as an Alternative to Cluster Analysis: An Application to Direct Marketing." International Journal of Market Research 45, no. 2 (2003): 1–20. http://dx.doi.org/10.1177/147078530304500205.
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This paper examines the potential of the Kohonen self-organising map (SOM) in a marketing context. It deals specifically with consumer attitudes towards direct marketing. The SOM belongs to the general class of neural network (NN) models, but differs from the now orthodox way in which NNs are implemented. The major difference is that network learning is ‘unsupervised’, in which case the SOM is related to clustering methods. The result of an SOM is a two-dimensional grid of related ‘prototypes’ rather than non-overlapping clusters. The method involves iterative adjustment of the prototypes in such a way as to capture and preserve the properties of the data. We show how the resulting maps offer useful new perspectives.
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Haese, Karin, and Geoffrey J. Goodhill. "Auto-SOM: Recursive Parameter Estimation for Guidance of Self-Organizing Feature Maps." Neural Computation 13, no. 3 (2001): 595–619. http://dx.doi.org/10.1162/089976601300014475.
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An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achieve good performance. Here we present the Auto-SOM, an algorithm that estimates the learning parameters during the training of SOMs automatically. The application of Auto-SOM provides the facility to avoid neighborhood violations up to a user-defined degree in either mapping direction. Auto-SOM consists of a Kalman filter implementation of the SOM coupled with a recursive parameter estimation method. The Kalman filter trains the neurons' weights with estimated learning coefficients so as to minimize the variance of the estimation error. The recursive parameter estimation method estimates the width of the neighborhood function by minimizing the prediction error variance of the Kalman filter. In addition, the “topographic function” is incorporated to measure neighborhood violations and prevent the map's converging to configurations with neighborhood violations. It is demonstrated that neighborhoods can be preserved in both mapping directions as desired for dimension-reducing applications. The development of neighborhood-preserving maps and their convergence behavior is demonstrated by three examples accounting for the basic applications of self-organizing feature maps.
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Hanumantha Rao, T. V. K., Saurabh Mishra, and Sudhir Kumar Singh. "Automatic Electrocardiographic Analysis Using Artificial Neural Network Models." Advanced Materials Research 403-408 (November 2011): 3587–93. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3587.
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In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.
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Urbański, Krzysztof, and Stanisław Gruszczyński. "Adaptive modelling of spatial diversification of soil classification units." Journal of Water and Land Development 30, no. 1 (2016): 127–39. http://dx.doi.org/10.1515/jwld-2016-0029.
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AbstractThe article presents the results of attempts to use adaptive algorithms for classification tasks different soils units. The area of study was the Upper Silesian Industrial Region, which physiographic and soils parameters in the form of digitized was used in the calculation. The study used algorithms, self-organizing map (SOM) of Kohonen, and classifiers: deep neural network, and two types of decision trees: Distributed Random Forest and Gradient Boosting Machine. Especially distributed algorithm Random Forest (algorithm DRF) showed a very high degree of generalization capabilities in modeling complex diversity of soil. The obtained results indicate, that the digitization of topographic and thematic maps give you a fairly good basis for creating useful models of soil classification. However, the results also showed that it cannot be concluded that the best algorithm presented in this research can be regarded as a general principle of system design inference.
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Tambouratzis, Tatiana, Dina Chernikova, and Imre Pzsit. "Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 3, no. 2 (2013): 77–88. http://dx.doi.org/10.2478/jaiscr-2014-0006.
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Abstract The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n's) and gamma rays (γ’s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the training set. the SOM exhibits higher n/γ identification rales when classifying new paltms regardless of the proportion of training and test set patterns at the different energy levels: the average tint: for decision making equals ∼100 /e in the cax of the LVQ and ∼450 μs in the case of the SOM.
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Alcan, Veysel, Hilal Kaya, Murat Zinnuroğlu, Gülçin Kaymak Karataş, and Mehmet Rahmi Canal. "A novel approach to the diagnostic assessment of carpal tunnel syndrome based on the frequency domain of the compound muscle action potential." Biomedical Engineering / Biomedizinische Technik 65, no. 1 (2020): 61–71. http://dx.doi.org/10.1515/bmt-2018-0077.
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AbstractConventional electrophysiological (EP) tests may yield ambiguous or false-negative results in some patients with signs and symptoms of carpal tunnel syndrome (CTS). Therefore, researchers tend to investigate new parameters to improve the sensitivity and specificity of EP tests. We aimed to investigate the mean and maximum power of the compound muscle action potential (CMAP) as a novel diagnostic parameter, by evaluating diagnosis and classification performance using the supervised Kohonen self-organizing map (SOM) network models. The CMAPs were analyzed using the fast Fourier transform (FFT). The mean and maximum power parameters were calculated from the power spectrum. A counter-propagation artificial neural network (CPANN), supervised Kohonen network (SKN) and XY-fused network (XYF) were compared to evaluate the classification and diagnostic performance of the parameters using the confusion matrix. The mean and maximum power of the CMAP were significantly lower in patients with CTS than in the normal group (p < 0.05), and the XYF network had the best total performance of classification with 91.4%. This study suggests that the mean and maximum power of the CMAP can be considered as less time-consuming parameters for the diagnosis of CTS without using additional EP tests which can be uncomfortable for the patient due to poor tolerance to electrical stimulation.
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Kosugi, Atsushi, Kok Hoong Leong, Eri Urata, et al. "Effect of Different Direct Compaction Grades of Mannitol on the Storage Stability of Tablet Properties Investigated Using a Kohonen Self-Organizing Map and Elastic Net Regression Model." Pharmaceutics 12, no. 9 (2020): 886. http://dx.doi.org/10.3390/pharmaceutics12090886.
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This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25 °C, 75% relative humidity for 1 week. Then, measurement of the tablet properties was conducted on both pre- and post-storage tablets. The tablet properties were tensile strength (TS), friability, and disintegration time (DT). The experimental data were analyzed using a Kohonen self-organizing map (SOM). The SOM analysis successfully classified the test grades into three distinct clusters having different changes in the behavior of the tablet properties accompanying storage. Cluster 1 showed an obvious rise in DT induced by storage, while cluster 3 showed a substantial change in mechanical strength of the tablet including a reduction in the TS and a rise in friability. Furthermore, the data were analyzed using an Elastic net regression technique to investigate the general relationships between the powder properties of mannitol and the change behavior of the tablet properties. Consequently, we succeeded in identifying the crucial powder properties for the storage stability of the resulting tablets. This study provides advanced technical knowledge to characterize the effect of different direct compaction grades of mannitol on the storage stability of tablet properties.
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Ewert, Pawel, Teresa Orlowska-Kowalska, and Kamila Jankowska. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks." Energies 14, no. 3 (2021): 712. http://dx.doi.org/10.3390/en14030712.
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Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.
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Cheh, John J., Evgeny A. Lapshin, and Il-Woon Kim. "An Application of Self-Organizing Maps to Financial Structure Analysis of Keiretsu versus Non-Keiretsu Firms in Japan." Review of Pacific Basin Financial Markets and Policies 09, no. 03 (2006): 405–29. http://dx.doi.org/10.1142/s0219091506000781.
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It has been argued that keiretsu in Japan allows its member firms to maintain a financial structure different from that of non-keiretsu member firms. In this paper, we use two different types of financial statement ratio analysis techniques to discover whether Kohonen's self-organizing map (SOM) is able to uncover the differences in financial structures between keiretsu and non-keiretsu firms: ad hoc financial ratios and valuation-based financial ratios. We have found some evidence that SOM enables both financial analysis techniques to recognize different financial structures between the two groups of the firms. Implications of this finding for investment decisions have been discussed.
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Koishi, M., and Z. Shida. "Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2." Tire Science and Technology 34, no. 3 (2006): 170–94. http://dx.doi.org/10.2346/1.2345640.
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Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.
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Assal, Samy F. M. "Self-organizing approach for learning the forward kinematic multiple solutions of parallel manipulators." Robotica 30, no. 6 (2011): 951–61. http://dx.doi.org/10.1017/s0263574711001172.
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SUMMARYContrary to the inverse kinematics, the forward kinematics of parallel manipulators involves solving highly non-linear equations and provides more than one feasible end-effector pose, which are called the assembly modes, for a given set of link lengths or joint angles. Out of the multiple feasible solutions, only one solution can be achieved from a certain initial configuration. Therefore, in this paper, the Kohonen's self-organizing map (SOM) is proposed to learn and classify the multiple solution branches of the forward kinematics and then provide a unique real-time solution among the assembly modes. Each solution of the multiple feasible ones is coded using IF-THEN rules based on the values of the passive joint variables. Due to not only the classification but also the associative memory learning abilities of the SOM, the passive joint variables vector, the end-effector pose vector, and this class code are associated with the active joint variables vector constituting the input vector to the SOM in the offline learning phase. In the online testing phase, only the active joint variables vector and the class code are fed to the SOM to obtain the unique end-effector pose vector. The Jacobian matrix calculated at the SOM output layer is used for further fine tuning this output to obtain an accurate end-effector pose vector. Simulations are conducted for 3-RPR and 3-RRR planar parallel manipulators to evaluate the performance of the proposed method. The results proved high accuracy of the desired unique solution in real-time.
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Mulier, Filip, and Vladimir Cherkassky. "Self-Organization as an Iterative Kernel Smoothing Process." Neural Computation 7, no. 6 (1995): 1165–77. http://dx.doi.org/10.1162/neco.1995.7.6.1165.
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Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.
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Abarca-Alvarez, Campos-Sánchez, and Mora-Esteban. "Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children's Perception of Urban School Environments." Entropy 21, no. 9 (2019): 916. http://dx.doi.org/10.3390/e21090916.
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The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied in many fields, facilitating the graphical representation and the simple interpretation of high-dimensionality data. This clustering method, based on unsupervised learning, also allows obtaining profiles of respondents without the need to provide additional information for the creation of these clusters. In this work, we propose the identification of profiles using SOM for evaluating opinion surveys. Subsequently, non-parametric chi-square tests were first conducted to contrast whether answer was independent of each profile found, and in the case of statistical significance (p ≤ 0.05), the odds ratio was evaluated as an indicator of the effect size of such dependence. Finally, all results were displayed in an odds and cluster heat map so that they could be easily interpreted and used to make decisions regarding the survey results. The methodology was applied to the analysis of a survey based on forms administered to children (N = 459) about their perception of the urban environment close to their school, obtaining relevant results, facilitating results interpretation, and providing support to the decision-process.
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Harchli, Fidae, Zakariae En-Naimani, Abdelatif Es-Safi, and Mohamed Ettaouil. "Vector Quantization for Speech Compression by a New Version of PRSOM." International Journal on Artificial Intelligence Tools 27, no. 03 (2018): 1850013. http://dx.doi.org/10.1142/s0218213018500136.
Full textAbstract:
The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Especially, it provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons, their initial weights vector and their deviation matrix is still a big problem in the literature. These parameters have a great impact on the learning process of the network, the convergence and the quality of results. Also determination of clusters’ number is a very difficult task. In this paper we propose a new method, called H-PRSOM, which looks for the optimal architecture of the map and determines the suitable codebook for speech compression. According to his hierarchical process, H-PRSOM identifies automatically, in each iteration, new initial parameters of the map. The generated parameters will be used in the learning phase of the probabilistic network. Due to its important propriety of initialization and optimization, we expect that the use of this new version of PRSOM algorithm in the vector quantization might provide good results. In order to evaluate its performance, H-PRSOM model is applied to the problem of speech compression of Arabic digits. The conducted experiments show that the proposed method is able to realize the expected goals.
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Kauko, Tom. "Using the Self-Organising Map to Identify Regularities across Country-Specific Housing-Market Contexts." Environment and Planning B: Planning and Design 32, no. 1 (2005): 89–110. http://dx.doi.org/10.1068/b3186.
Full textAbstract:
The aim of exploring and monitoring housing-market fundamentals (prices, dwelling features, area density, residents, and so on) on a macrolocational level relates to both public and private sector policymaking. Housing market segmentation (that is, the emergence of housing submarkets), a concept with increasing relevance, is defined as the differentiation of housing in terms of the income and preferences of the residents and in terms of administrative circumstances. In order to capture such segmentation empirically, the author applies a fairly new and emerging technique known as the ‘self-organising’ map (SOM), or ‘Kohonen map’. The SOM is a type of (artificial) neural network—a nonlinear and flexible (that is, nonparametric or semiparametric) regression and ‘machine learning’ technique. By utilising the ability of the SOM to visualise patterns, one can analyse various dimensions within the variation of the dataset. Segmentation may then be detected depending on the resulting patterns across the map layers, each of which represents the data variation for one input variable. Utilising an inductive modelling strategy, the author runs cross-sectional and nationwide data on the owner-occupied housing markets of Finland (documentation presented elsewhere), the Netherlands, and Hungary with the SOM technique. On the basis of the resulting configurations certain regularities (similarities and differences) across the three national contexts are identified. In all three cases the segments are determined by physical and institutional differences between the housing bundles and localities. The exercise demonstrates how the inductive SOM-based approach is well-suited for illustrating the contextual factors that determine housing market structure.
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Peeters, L., F. Bação, V. Lobo, and A. Dassargues. "Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map." Hydrology and Earth System Sciences Discussions 3, no. 4 (2006): 1487–516. http://dx.doi.org/10.5194/hessd-3-1487-2006.
Full textAbstract:
Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 141 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. The standard SOM proves to be more adequate in representing the structure of the data set and to explore relationships between variables. The GEO3DSOM on the other hand performs better in creating spatially coherent groups based on the data.
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Dragomir, Andrei, Seferina Mavroudi, and Anastasios Bezerianos. "Som-Based Class Discovery Exploring the ICA-Reduced Features of Microarray Expression Profiles." Comparative and Functional Genomics 5, no. 8 (2004): 596–616. http://dx.doi.org/10.1002/cfg.444.
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Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm automatically determines the number of ‘natural’ subgroups of the data, being aided at this task by the available prior knowledge of the functional categories of genes. An entropy criterion allows each gene to be assigned to multiple classes, which is closer to the biological representation. These features, however, are not achieved at the cost of the simplicity of the algorithm, since the map grows on a simple grid structure and the learning algorithm remains equal to Kohonen’s one.
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Galvin, T. J., M. T. Huynh, R. P. Norris, et al. "Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era." Monthly Notices of the Royal Astronomical Society 497, no. 3 (2020): 2730–58. http://dx.doi.org/10.1093/mnras/staa1890.
Full textAbstract:
ABSTRACT We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting Parallelized rotation and flipping INvariant Kohonen maps (pink), a self-organizing map (SOM) algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using 894 415 images from the Faint Images of the Radio-Sky at Twenty centimeters (FIRST) and Wide-field Infrared Survey Explorer (WISE) surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802 646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products, we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. ‘X’-shaped galaxies, hybrid FR I/FR II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host, (iii) introduce a ‘curliness’ statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700 and 1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.