Relevant bibliographies by topics / Multilayer self organizing neural network

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Academic literature on the topic 'Multilayer self organizing neural network'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Multilayer self organizing neural network"

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Yu, Xiujin, Shengfu Liu, and Hui Zhang. "Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques." Computational Intelligence and Neuroscience 2021 (October 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/4105784.

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As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural ne
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Pakhomova, V., and A. Vydish. "Study of the combined variant of determination of attacks using neural network technologies." System technologies 3, no. 140 (2022): 79–86. http://dx.doi.org/10.34185/1562-9945-3-140-2022-08.

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The modern world is impossible to imagine without computer networks: both local and global; therefore, the issue of network security is becoming increasingly topical. Currently, methods of detecting attacks can be strengthened by using neural networks, which confirms the relevance of the topic. The aim of the study is a comparative analysis of the quality parameters of network attacks using a combined variant consisting of different neural networks. As research methods used: neural network; multilayer perceptron; Kohonen's self-organizing map. The software implementation of the Kohonen self-or
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Al-Khasawneh, Ahmad. "Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques." International Journal of E-Health and Medical Communications 7, no. 1 (2016): 65–75. http://dx.doi.org/10.4018/ijehmc.2016010104.

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Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilaye
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Han, Hong-Gui, Li-Dan Wang, and Jun-Fei Qiao. "Efficient self-organizing multilayer neural network for nonlinear system modeling." Neural Networks 43 (July 2013): 22–32. http://dx.doi.org/10.1016/j.neunet.2013.01.015.

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Bhattacharyya, Siddhartha, Pankaj Pal, and Sandip Bhowmick. "Binary image denoising using a quantum multilayer self organizing neural network." Applied Soft Computing 24 (November 2014): 717–29. http://dx.doi.org/10.1016/j.asoc.2014.08.027.

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Sarukkai, Ramesh R. "Supervised Networks That Self-Organize Class Outputs." Neural Computation 9, no. 3 (1997): 637–48. http://dx.doi.org/10.1162/neco.1997.9.3.637.

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Supervised, neural network, learning algorithms have proved very successful at solving a variety of learning problems; however, they suffer from a common problem of requiring explicit output labels. In this article, it is shown that pattern classification can be achieved, in a multilayered, feedforward, neural network, without requiring explicit output labels, by a process of supervised self-organization. The class projection is achieved by optimizing appropriate within-class uniformity and between-class discernibility criteria. The mapping function and the class labels are developed together
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Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs
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Stepanyan, Ivan, Sergey Grokhovsky, and Mikhail Savkin. "Identification of pathobiomechanical markers of statokinesiograms on the example of neural network identification of a post-stroke state." Russian journal of biomechanics. 27, no. 1 (2023): 84–93. http://dx.doi.org/10.15593/rjbiomech/2023.1.09.

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The purpose of this study is neural network modeling and determination of the parameters of statokinesiograms, which are carriers of useful information about the features of postural regulation, which determined the obtained trajectory of movements of the human center of mass. A technique for obtaining informative markers by identifying clustering centroids based on self-organizing Kohonen neural networks with the Euclidean metric has been developed. Kohonen networks trained without a teacher (that is, without the use of a priori diagnostic information about the state of the subjects) are a po
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Minnix, J. I., E. S. McVey, and R. M. Inigo. "A multilayered self-organizing artificial neural network for invariant pattern recognition." IEEE Transactions on Knowledge and Data Engineering 4, no. 2 (1992): 162–67. http://dx.doi.org/10.1109/69.134253.

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BHATTACHARYA, UJJWAL, TANMOY KANTI DAS, AMITAVA DATTA, SWAPAN KUMAR PARUI, and BIDYUT BARAN CHAUDHURI. "A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (2002): 845–64. http://dx.doi.org/10.1142/s0218001402002027.

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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uni
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Dissertations / Theses on the topic "Multilayer self organizing neural network"

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Sun, Zhibin. "Application of artificial neural networks in early detection of Mastitis from improved data collected on-line by robotic milking stations." Lincoln University, 2008. http://hdl.handle.net/10182/665.

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Two types of artificial neural networks, Multilayer Perceptron (MLP) and Self-organizing Feature Map (SOM), were employed to detect mastitis for robotic milking stations using the preprocessed data relating to the electrical conductivity and milk yield. The SOM was developed to classify the health status into three categories: healthy, moderately ill and severely ill. The clustering results were successfully evaluated and validated by using statistical techniques such as K-means clustering, ANOVA and Least Significant Difference. The result shows that the SOM could be used in the robotic milki
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Niebur, Dagmar. "Kohonen self-organizing neural network for power system security assessment /." [S.l.] : [s.n.], 1994. http://library.epfl.ch/theses/?nr=1244.

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Tinós, Renato. "Detecção e diagnóstico de falhas em robôs manipuladores via redes neurais artificiais." Universidade de São Paulo, 1999. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-04022002-162950/.

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Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usa
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Quintin, G. M. M. "Implementation of self-organizing maps neural networks on network parallel computers." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0004/MQ44855.pdf.

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Goodman, Stephen D. "Temporal pattern identification in a self-organizing neural network with an application to data compression." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15794.

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Lundberg, Emil. "Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.

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Vector Quantization (VQ) is a classic optimization problem and a simple approach to pattern recognition. Applications include lossy data compression, clustering and speech and speaker recognition. Although VQ has largely been replaced by time-aware techniques like Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW) in some applications, such as speech and speaker recognition, VQ still retains some significance due to its much lower computational cost — especially for embedded systems. A recent study also demonstrates a multi-section VQ system which achieves performance rivaling that of
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Edler, Lars [Verfasser]. "Analysing Economic Data with Self-Organizing Maps : A Geometric Neural Network Approach / Lars Edler." Kiel : Universitätsbibliothek Kiel, 2008. http://d-nb.info/1019667230/34.

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Vidinski, Phillip T., and Phillip T. Vidinski. "Neural Network Force Control of a Spherical Parallel Wrist." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625324.

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This thesis introduces an orienting mechanism and control system for the purpose of eye tonometry. The design is based on a 3RRR spherical parallel manipulator architecture. The end-effector is mounted with a triad of force sensing elements. Presented in this paper is a unique approach to force control based on an artificial neural network. The mechanism generates movements to collect data about its tactile environment ultimately generating a path to the force sensors' equilibrium point.
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Goyal, Arvind. "APPLICATION OF ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR DESIGN OF MODULAR MINICELL CONFIGURATIONS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/508.

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Artificial neural networks, so far, have not been used for designing modular cells. Therefore, Self-organizing neural network (SONN) is used in the present research to design minicell-based manufacturing system. Two previously developed methods were studied and implemented using SONN model. Results obtained are compared with previous results to analyze the effectiveness of SONN in designing minicells. A new method is then developed with the objective to design minicells more effectively and efficiently. Results of all three methods are compared using machine-count and materialhandling as perfo
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Li, Chien-Kuo. "New neural network structures for problems with high-dimensional input space /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841317.

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Books on the topic "Multilayer self organizing neural network"

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Serrano-Cinca, Carlos. From financial information to strategic groups: A self-organizing neural network approach. University of Southampton, 1996.

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Book chapters on the topic "Multilayer self organizing neural network"

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Ouadfeul, Sid-Ali, and Leila Aliouane. "Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_87.

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Teshnehlab, Mohammad, and Keigo Watanabe. "Self-organizing Flexible Neural Network." In Intelligent Control Based on Flexible Neural Networks. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9187-4_8.

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Elmenreich, Wilfried, and István Fehérvári. "Evolving Self-organizing Cellular Automata Based on Neural Network Genotypes." In Self-Organizing Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19167-1_2.

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Yin, Yong, Ikou Kaku, Jiafu Tang, and JianMing Zhu. "Neural Network and Self-organizing Maps." In Data Mining. Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-338-1_5.

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Morasso, Pietro, Alberto Pareto, Stefano Pagliano, and Vittorio Sanguineti. "Self-Organizing Neural Network for Diagnosis." In ICANN ’93. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_228.

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Marshall, Jonathan A. "Representation of Uncertainty in Self-Organizing Neural Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_95.

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Cherkassky, Vladimir, and Hossein Lari-Najafi. "Self-Organizing Neural Network for Non-Parametric Regression Analysis." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_108.

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Grossberg, Stephen. "Self-Organizing Neural Architectures for Vision, Learning, and Robotic Control." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_92.

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Zhang, Tianyue, Baile Xu, and Furao Shen. "Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clustering." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_3.

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Shen, Furao, and Osamu Hasegawa. "Self-Organizing Incremental Neural Network and Its Application." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_74.

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Conference papers on the topic "Multilayer self organizing neural network"

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Geng, Zhili, Wei Liu, and Cuili Yang. "Self-organizing Recurrent Fuzzy Neural Network for Nonlinear System Modeling." In 2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA). IEEE, 2024. http://dx.doi.org/10.1109/ispce-asia64773.2024.10756219.

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Chen, Hongzhou, Bin Hu, Long Chen, Dingxue Zhang, and Zhihong Guan. "Multilayer Self-Organizing Impulse Neural Network For Handwritten Digit Recognition." In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2021. http://dx.doi.org/10.1109/ddcls52934.2021.9455541.

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Bhattacharyya, Siddhartha, Pankaj Pal, and Sandip Bhowmik. "A Quantum Multilayer Self Organizing Neural Network for Object Extraction from a Noisy Background." In 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.108.

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Klose, Joerg, and Oliver Altena. "Feature trajectory reduction of integrated autoregressive processes based on a multilayer self-organizing neural network." In San Diego, '91, San Diego, CA, edited by Simon Haykin. SPIE, 1991. http://dx.doi.org/10.1117/12.49802.

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Ghaleb, Moshira S., Hala M. Ebied, Howida A. Shedeed, and Mohamed F. Tolba. "Image Retrieval Based on Self-Organizing Feature Map and Multilayer Perceptron Neural Networks Classifier." In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014768.

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Sudakov, O. V., and D. V. Dmitriev. "Comparison of G-Means Algorithms and Kohonen Network in Solving Clustering Problems." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-1147-1156.

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Purpose: In this paper, the question of how to improve a self-organizing neural network consisting of a bundle of clustering algorithm and a multilayer perceptron for data verification tasks in the absence of training pairs is considered. Design/methodology/approach: The most popular clustering algorithm is the Kohonen network, but today it is not the only algorithm capable of performing the task quickly and accurately. The paper compares the Kohonen network and the G-Means algorithm. The principle of operation of these two algorithms is briefly analyzed. The accuracy of these algorithms and t
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Rigui Zhou, Hongyuan Zheng, Nan Jiang, and Qiulin Ding. "Self-Organizing Quantum Neural Network." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246807.

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Jun-Fei Qiao, Hong-Gui Han, and Yan-Mei Jia. "An adaptive self-organizing fuzzy neural network." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420761.

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Marshall, J. A. "A self-organizing scale-sensitive neural network." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137911.

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Castellano, G., and A. M. Fanelli. "A self-organizing neural fuzzy inference network." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861428.

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