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Akbar Maulana and Enny Itje Sela. "The Implementation of Artificial Neural Networks for Stock Price Prediction." Journal of Engineering, Electrical and Informatics 3, no. 3 (2023): 34–44. http://dx.doi.org/10.55606/jeei.v3i3.2254.
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This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.
 
 Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
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LERNER, B., H. GUTERMAN, I. DINSTEIN, and Y. ROMEM. "HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK." International Journal of Neural Systems 06, no. 03 (1995): 359–70. http://dx.doi.org/10.1142/s012906579500024x.
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A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10–20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.
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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network." International Journal of Applied Evolutionary Computation 12, no. 3 (2021): 35–43. http://dx.doi.org/10.4018/ijaec.2021070103.
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Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.
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Kaur, Jatinder, Dr Mandeep Singh, Pardeep Singh Bains, and Gagandeep Singh. "Analysis of Multi layer Perceptron Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (2013): 600–606. http://dx.doi.org/10.24297/ijct.v7i2.3462.
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In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.
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Lazri, Mourad, Fethi Ouallouche, Karim Labadi, and Soltane Ameur. "Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data." E3S Web of Conferences 353 (2022): 01006. http://dx.doi.org/10.1051/e3sconf/202235301006.
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The application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to the size and dimension of the data used. Contrary to these considerations, a new neural network, such as Extreme Learning Machine (ELM) has recently been implemented. The ELM does not care much about the size of the neural network, the hidden layer parameters are randomly generated and remain constant instead of being adjusted during training. In this paper, we will present a comparison between two neural networks, namely ELM and MLP (Multilayer perceptron) implemented for the precipitation estimation from meteorological satellite data. The architecture chosen for the two neural networks consists of an input layer (7 neurons), a hidden layer (8 neurons) and an output layer (7 neurons). The MLP has undergone standard training as soon as the ELM is trained according to the characteristics mentioned above. The results show that MLP prevails over ELM. However, the time cost during learning is too high for MLP compared to ELM.
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Li, Deying, Faming Huang, Liangxuan Yan, Zhongshan Cao, Jiawu Chen, and Zhou Ye. "Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models." Applied Sciences 9, no. 18 (2019): 3664. http://dx.doi.org/10.3390/app9183664.
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Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.
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Lorençone, João Antonio, Pedro Antonio Lorençone, Lucas Eduardo Oliveira Aparecido, Guilherme Botega Torsoni, and Lucas da Rocha Ferreira. "NEURAL NETWORKS IN SIMULATING POTENTIAL CLIMATIC CONDITIONS FOR BAMBOO CULTIVATION IN BRAZIL." Revista Contemporânea 3, no. 10 (2023): 17822–31. http://dx.doi.org/10.56083/rcv3n10-064.
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This study aimed to perform the agricultural zoning of climatic risk for bamboo in Brazil by means of artificial neural networks. It was used climatic data of air temperature (TAIR, ºC) and rainfall (P). The Feed Forward Artificial Neural Network, Multilayer Perceptron (MLP) with backpropagation learning algorithm for multilayers was employed. The agroclimatic zoning allowed the classification of regions by climatic suitability and showed that 71% of the national territory was suitable for bamboo cultivation. The use of the neural network allowed an accurate and fast classification of climate suitability.
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Emedolu, Blessing Obianuju, Godwin Thomas, and Nentawe Y. Gurumdimma. "Phishing Website Detection using Multilayer Perceptron." International Journal of Research and Innovation in Applied Science VIII, no. VII (2023): 260–67. http://dx.doi.org/10.51584/ijrias.2023.8730.
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Phishing attacks pose a significant threat in the cyber world, exploiting unsuspecting users through deceptive emails that lead them to malicious websites. To combat this challenge, various deep learning based anti-phishing techniques have been developed. However, these models often suffer from high false positive rates or lower accuracy. In this study, we evaluate the performance of two neural networks, the Autoencoder and Multilayer Perceptron (MLP), using a publicly available dataset to build an efficient phishing detection model. Feature selection was performed through correlation analysis, and the Autoencoder achieved an accuracy of 94.17%, while the MLP achieved 96%. We used hyperparameters for optimization using the Gridsearch CV, resulting in a False Positive Rate (FPR) of 1.3%, outperforming the Autoencoder’s 4.1% FPR. The MLP model was further deployed to determine the legitimacy of websites based on input URLs, demonstrating its usability in real-world scenarios. This research contributes to the development of effective phishing detection models, emphasizing the importance of optimizing neural network architecture for improved accuracy and reduced false positives
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Li, Yong, Qidan Zhu, and Ahsan Elahi. "Quadcopter Trajectory Tracking Based on Model Predictive Path Integral Control and Neural Network." Drones 9, no. 1 (2024): 9. https://doi.org/10.3390/drones9010009.
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This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Predictive Path Integral controller. The Model Predictive Path Integral controller uses the model to control the quadcopter following the desired trajectory. Experimental data show that the improved Model Predictive Path Integral–Multilayer Perceptron method reduces the trajectory tracking error by 10.3%, 9.8%, and 5.7% compared to the traditional Model Predictive Path Integral, MPC with MLP, and a two-layer network, respectively. These results demonstrate the potential application of the method in complex environments.
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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.
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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.
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Kovács, László. "Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures." Mathematics 13, no. 9 (2025): 1471. https://doi.org/10.3390/math13091471.
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The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures.
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Buevich, A. G., I. E. Subbotina, A. V. Shichkin, A. P. Sergeev, and E. M. Baglaeva. "Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics." Геоэкология. Инженерная геология. Гидрогеология. Геокриология, no. 2 (May 18, 2019): 77–86. http://dx.doi.org/10.31857/s0869-78092019277-86.
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Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.
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Anggraeni, Dewi, and Sri Rezki Maulina Azmi. "ANALYSIS OF NEURAL NETWORK ALGORITHM IN URBAN AIR QUALITY PREDICTION." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 2 (2025): 375–80. https://doi.org/10.33330/jurteksi.v11i2.3822.
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Abstract: Air quality in urban areas is becoming an increasingly important issue considering its impact on human health and the environment. The rapid increase in air pollution requires effective methods to predict air quality in order to take appropriate mitigation measures. This study aims to analyze the use of Neural Network (NN) algorithms in predicting air quality in cities. The method used is the application of the NN model, especially the Multilayer Perceptron (MLP), which is trained using historical air quality data such as dust particle levels (PM10, PM2.5), carbon monoxide (CO) gas, and temperature. The data used in this study came from urban air quality monitoring stations collected over a period of time. The results show that the Neural Network algorithm can provide quite accurate predictions of air quality with a low Mean Absolute Error (MAE) value, showing the effectiveness of the model in predicting f fluctuations in air quality. The conclusion of this study is that Neural Network algorithms, specifically MLPs, are an effective tool for air quality prediction, which can be used as a basis for urban air quality management policies. Keywords: air quality; neural network; prediction; multilayer perceptron (MLP) Abstrak: Kualitas udara di perkotaan menjadi isu yang semakin penting mengingat dampaknya terhadap kesehatan manusia dan lingkungan. Peningkatan polusi udara yang pesat memerlukan metode yang efektif untuk memprediksi kualitas udara guna mengambil langkah mitigasi yang tepat. Penelitian ini bertujuan untuk menganalisis penggunaan algoritma Neural Network (NN) dalam memprediksi kualitas udara di perkotaan. Metode yang digunakan adalah penerapan model NN, khususnya Multilayer Perceptron (MLP), yang dilatih menggunakan data kualitas udara historis seperti kadar partikel debu (PM10, PM2.5), gas karbon monoksida (CO), dan suhu. Data yang digunakan dalam penelitian ini berasal dari stasiun pemantauan kualitas udara di perkotaan yang dikumpulkan selama periode waktu tertentu. Hasil penelitian menunjukkan bahwa algoritma Neural Network dapat memberikan prediksi yang cukup akurat terhadap kualitas udara dengan nilai Mean Absolute Error (MAE) yang rendah, menunjukkan efektivitas model dalam memprediksi fluktuasi kualitas udara. Simpulan dari penelitian ini adalah bahwa algoritma Neural Network, khususnya MLP, merupakan alat yang efektif untuk prediksi kualitas udara, yang dapat digunakan sebagai dasar untuk kebijakan pengelolaan kualitas udara di perkotaanKata kunci: kualitas udara; neural network; prediksi; multilayer perceptron (MLP)
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Govindarajan, M., and RM Chandrasekaran. "A Hybrid Multilayer Perceptron Neural Network for Direct Marketing." International Journal of Knowledge-Based Organizations 2, no. 3 (2012): 63–73. http://dx.doi.org/10.4018/ijkbo.2012070104.
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Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
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Du, Ke-Lin, Chi-Sing Leung, Wai Ho Mow, and M. N. S. Swamy. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era." Mathematics 10, no. 24 (2022): 4730. http://dx.doi.org/10.3390/math10244730.
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The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it is incapable of classifying linearly inseparable patterns. A new era of neural network research started in 1986, when the backpropagation (BP) algorithm was rediscovered for training the multilayer perceptron (MLP) model. An MLP with a large number of hidden nodes can function as a universal approximator. To date, the MLP model is the most fundamental and important neural network model. It is also the most investigated neural network model. Even in this AI or deep learning era, the MLP is still among the few most investigated and used neural network models. Numerous new results have been obtained in the past three decades. This survey paper gives a comprehensive and state-of-the-art introduction to the perceptron model, with emphasis on learning, generalization, model selection and fault tolerance. The role of the perceptron model in the deep learning era is also described. This paper provides a concluding survey of perceptron learning, and it covers all the major achievements in the past seven decades. It also serves a tutorial for perceptron learning.
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Galchonkov, Oleg, Oleksii Baranov, Oleh Maslov, Mykola Babych, and Illia Baskov. "MLP-KAN: implementation of the Kolmogorov-Arnold layer in a multilayer perceptron." Eastern-European Journal of Enterprise Technologies 3, no. 4 (135) (2025): 34–41. https://doi.org/10.15587/1729-4061.2025.328928.
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The object of this study is neural networks used for categorizing objects in images. The task addressed in the work is to identify options for building a multilayer perceptron architecture that apply the Kolmogorov-Arnold layer and are characterized by the best ratio of classification quality and computational effort. The paper proposes a modification to the multilayer perceptron (MLP) by replacing the first hidden layer with a Kolmogorov-Arnold layer. This allowed the use of the approximating properties of neurons and learning activation functions simultaneously. A feature of the designed MLP-KAN neural network, unlike the classical KAN network, is the use of only one activation function for each of the input elements. The training of activation functions is carried out on the basis of invariant radial basis functions, which are composed using learning weight coefficients. Such construction of the MLP-KAN neural network architecture allowed the use of typical libraries and optimizers for its training. In this case, unlike known analogs, there is no slowdown in the learning speed. Experimental studies on the handwritten digit dataset (MNIST) have shown that MLP-KAN could provide higher classification quality with less computational effort. In particular, to obtain classification quality comparable to MLP, with the appropriate parameter setting, MLP-KAN requires 3.63 times less computational effort than MLP. This makes it possible to significantly improve the efficiency of image object classification devices built on microprocessors operating under an autonomous mode as part of robotic systems
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Journal, Baghdad Science. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.7.2.1076-1081.
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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.
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Mazher, Alaa noori, and Samira faris Khlibs. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.2010.7.2.1076-1081.
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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.
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Ahsan, Ahmad Omar, Susanna Tang, and Wei Peng. "Efficient Hyperbolic Perceptron for Image Classification." Electronics 12, no. 19 (2023): 4027. http://dx.doi.org/10.3390/electronics12194027.
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Deep neural networks, often equipped with powerful auto-optimization tools, find widespread use in diverse domains like NLP and computer vision. However, traditional neural architectures come with specific inductive biases, designed to reduce parameter search space, cut computational costs, or introduce domain expertise into the network design. In contrast, multilayer perceptrons (MLPs) offer greater freedom and lower inductive bias than convolutional neural networks (CNNs), making them versatile for learning complex patterns. Despite their flexibility, most neural architectures operate in a flat Euclidean space, which may not be optimal for various data types, particularly those with hierarchical correlations. In this paper, we move one step further by introducing the hyperbolic Res-MLP (HR-MLP), an architecture extending the attention-free MLP to a non-Euclidean space. HR-MLP leverages fully hyperbolic layers for feature embeddings and end-to-end image classification. Our novel Lorentz cross-patch and cross-channel layers enable direct hyperbolic operations with fewer parameters, facilitating faster training and superior performance compared to Euclidean counterparts. Experimental results on CIFAR10, CIFAR100, and MiniImageNet confirm HR-MLP’s competitive and improved performance.
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Back, Andrew D., and Ah Chung Tsoi. "An Adaptive Lattice Architecture for Dynamic Multilayer Perceptrons." Neural Computation 4, no. 6 (1992): 922–31. http://dx.doi.org/10.1162/neco.1992.4.6.922.
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Time-series modeling is a topic of growing interest in neural network research. Various methods have been proposed for extending the nonlinear approximation capabilities to time-series modeling problems. A multilayer perceptron (MLP) with a global-feedforward local-recurrent structure was recently introduced as a new approach to modeling dynamic systems. The network uses adaptive infinite impulse response (IIR) synapses (it is thus termed an IIR MLP), and was shown to have good modeling performance. One problem with linear IIR filters is that the rate of convergence depends on the covariance matrix of the input data. This extends to the IIR MLP: it learns well for white input signals, but converges more slowly with nonwhite inputs. To solve this problem, the adaptive lattice multilayer perceptron (AL MLP), is introduced. The network structure performs Gram-Schmidt orthogonalization on the input data to each synapse. The method is based on the same principles as the Gram-Schmidt neural net proposed by Orfanidis (1990b), but instead of using a network layer for the orthogonalization, each synapse comprises an adaptive lattice filter. A learning algorithm is derived for the network that minimizes a mean square error criterion. Simulations are presented to show that the network architecture significantly improves the learning rate when correlated input signals are present.
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ANDERSEN, TIMOTHY L., and TONY R. MARTINEZ. "DMP3: A DYNAMIC MULTILAYER PERCEPTRON CONSTRUCTION ALGORITHM." International Journal of Neural Systems 11, no. 02 (2001): 145–65. http://dx.doi.org/10.1142/s0129065701000576.
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This paper presents DMP3 (Dynamic Multilayer Perceptron 3), a multilayer perceptron (MLP) constructive training method that constructs MLPs by incrementally adding network elements of varying complexity to the network. DMP3 differs from other MLP construction techniques in several important ways, and the motivation for these differences are given. Information gain rather than error minimization is used to guide the growth of the network, which increases the utility of newly added network elements and decreases the likelihood that a premature dead end in the growth of the network will occur. The generalization performance of DMP3 is compared with that of several other well-known machine learning and neural network learning algorithms on nine real world data sets. Simulation results show that DMP3 performs better (on average) than any of the other algorithms on the data sets tested. The main reasons for this result are discussed in detail.
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Dawson, C. W., C. Harpham, R. L. Wilby, and Y. Chen. "Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China." Hydrology and Earth System Sciences 6, no. 4 (2002): 619–26. http://dx.doi.org/10.5194/hess-6-619-2002.
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Abstract. While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
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Garcia, Samuel, and Mingjun Zhang. "Higher-order HDL: Applied to MLP neural network hardware implementation." E3S Web of Conferences 631 (2025): 02004. https://doi.org/10.1051/e3sconf/202563102004.
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In this article, we describe a methodology for the rapid implementation of a hardware architecture using a higher-order approach. This methodology uses a combination of TCL and VHDL for higher-order coding (i.e. code produced by code) and is supported by industry-standard HDL development tools. To explore this methodology, we used an FPGA implementation of an artificial neural network (ANN) as a guinea pig application. This enabled us to produce a fully generic multilayer perceptron model where the number of layers, the size of each layer, the types of synaptic signals and the activation function are easily customizable. Not only does this approach make the development of such an application faster, but the high degree of genericity of the model cannot be achieved with conventional VHDL methodology. This article presents feedback from our first steps with this methodology and its application to MLP hardware architecture. Index Terms—VHDL, TCL, Artificial Neural Networks, Multilayer Perceptron, higher-order programming, Methodology
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Gvishiani, Zurab, and Jacek Dawidowicz. "Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes." Rocznik Ochrona Środowiska 24 (2022): 505–19. http://dx.doi.org/10.54740/ros.2022.036.
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Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.
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Pradhan, Ananta Man Singh, and Yun-Tae Kim. "Landslide susceptibility mapping of Phewa catchment using multilayer perceptron artificial neural network." Nepal Journal of Environmental Science 4 (December 5, 2016): 1–9. http://dx.doi.org/10.3126/njes.v4i0.22718.
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The aim of this study was to prepare landslide susceptibility mapping technique using multilayer perceptron artificial neural network (MLP-ANN) and then to apply this method to Phewa catchment in western Nepal. To determine the effect of causative factors on landslides, data layers of aspect, elevation, slope, internal relief, slope shape, drainage proximity, drainage density, stream power index, topographic wetness index, sediment transport index, land cover and geology were analysed in R-statistical package and final map was produced using geographical information system environment. A GIS-based landslide inventory map of 88 landslide locations was prepared using data from previous reports and satellite image interpretation. A MLP-ANN model was generated from a training set consisting of ~70% randomly selected landslide in the inventory map, with the remaining ~30% landslides used for validation of the susceptibility map. According to analysis, the model had a success rate of 82.1% and the prediction accuracy of 91.4%, indicating a good performance.
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Todri, Ardita, and Petraq Papajorgji. "An Artificial Neural Network Growth Analysis in Construction Businesses." International Conference on Pioneer and Innovative Studies 1 (June 13, 2023): 1–8. http://dx.doi.org/10.59287/icpis.796.
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This research paper explores the firms' growths analysis through Artificial Neural Networks, explicitly using the Multilayer Perceptron (MLP) Analysis in a panel of construction businesses operating in the country. The construction businesses data used are classified into Organizational characteristics (5 patterns) and Financial indicators (18 patterns). They refer to Liquidity (5), Operational Efficiency (4), Leverage (4), and Growth (5) patterns. Thus, 85 construction business data from 2020-2021 have been collected, but only 31 businesses are considered valid for Multilayer Perceptron analysis training purposes. The first research step before building the multilayer perceptron neural network is the implementation of the Receiver Operating Characteristics (ROC curve) Analysis at a 95% confidence level, considering as a dependent variable the firms' age [in start-up (0); growth (1) and those in the maturity phase (2)]. Then, based on ROC analysis results, a multilayer perceptron network with 10 input layers patterns, 10 customers' patterns factors, and one covariate is implemented. The number of hidden layers is 1, and the number of units in hidden layers is 20. The activation function used is Hyperbolic tangent. Thus, the empirical findings of the research provide construction businesses and line ministries with valuable insights on boosting their growth.
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Yüksel, Esra, Derya Soydaner, and Hüseyin Bahtiyar. "Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron." International Journal of Modern Physics E 30, no. 03 (2021): 2150017. http://dx.doi.org/10.1142/s0218301321500178.
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In recent years, artificial neural networks and their applications for large data sets have become a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial neural network (ANN), to predict ground-state binding energies of atomic nuclei. Two different MLP architectures with three and four hidden layers are used to study their effects on the predictions. To train the MLP architectures, two different inputs are used along with the latest atomic mass table and changes in binding energy predictions are also analyzed in terms of the changes in the input channel. It is seen that using appropriate MLP architectures and putting more physical information in the input channels, MLP can make fast and reliable predictions for binding energies of atomic nuclei, which is also comparable to the microscopic energy density functionals.
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Mahmoudi, Jamal, Mohammad Ali Arjomand, Masoud Rezaei, and Mohammad Hossein Mohammadi. "Predicting the Earthquake Magnitude Using the Multilayer Perceptron Neural Network with Two Hidden Layers." Civil Engineering Journal 2, no. 1 (2016): 1–12. http://dx.doi.org/10.28991/cej-2016-00000008.
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Because of the major disadvantages of previous methods for calculating the magnitude of the earthquakes, the neural network as a new method is examined. In this paper a kind of neural network named Multilayer Perceptron (MLP) is used to predict magnitude of earthquakes. MLP neural network consist of three main layers; input layer, hidden layer and output layer. Since the best network configurations such as the best number of hidden nodes and the most appropriate training method cannot be determined in advance, and also, overtraining is possible, 128 models of network are evaluated to determine the best prediction model. By comparing the results of the current method with the real data, it can be concluded that MLP neural network has high ability in predicting the magnitude of earthquakes and it’s a very good choice for this purpose.
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LEHTOKANGAS, MIKKO. "FAST LEARNING USING MULTILAYER PERCEPTRON NETWORKS WITH ADAPTIVE CENTROID LAYER." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 02 (2000): 211–23. http://dx.doi.org/10.1142/s0218001400000143.
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A hybrid neural network architecture is investigated for classification purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers the first hidden layer is selected to be an adaptive centroid layer. Each unit in this new layer incorporates a centroid vector that is located somewhere in the space spanned by the input variables. The output of these units is the Euclidean distance between the centroid vector and the inputs. The centroid layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of the MLP and RBF networks. The presented benchmark experiments demonstrate that the proposed hybrid can provide significant advantages over standard MLPs in terms of fast and efficient learning, and compact network structure.
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Zambri, N. A., Norhafiz Salim, A. Mohamed, and Ili Najaa Aimi Mohd Nordin. "Modeling of a planar SOFC performances using artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1645. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1645-1652.
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The Planar Solid Oxide Fuel Cell (PSOFC) is one of the renewable energy technologies that is important as the main source for distributed generation and can play a significant role in the conventional electrical power generation. PSOFC stack modeling is performed in order to provide a platform for the optimal design of fuel cell systems. It is explained by the structure and operating principle of the PSOFC for the modeling purposes. PSOFC model can be developed using Artificial Neural Network approach. The data required to train the neural net-work model is generated by simulating the existing PSOFC model in the MATLAB/ Simulink software. The Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks are the most useful techniques in many applications and will be applied in developing the PSOFC model. A detailed analysis is presented on the best ANN network that gives the greatest results on the performances of the PSOFC. The simulation results show that Multilayer Perceptron (MLP) gives the best outcomes of the PSOFC performance based on the smallest errors and good regression analysis.
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Najeeb, Huda Dheyauldeen. "Artificial Neural Network for TIFF Image Compression." Ibn AL-Haitham Journal For Pure and Applied Sciences 30, no. 1 (2017): 246–61. http://dx.doi.org/10.30526/30.1.1074.
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The main aim of image compression is to reduce the its size to be able for transforming and storage, therefore many methods appeared to compress the image, one of these methods is "Multilayer Perceptron ". Multilayer Perceptron (MLP) method which is artificial neural network based on the Back-Propagation algorithm for compressing the image. In case this algorithm depends upon the number of neurons in the hidden layer only the above mentioned will not be quite enough to reach the desired results, then we have to take into consideration the standards which the compression process depend on to get the best results. We have trained a group of TIFF images with the size of (256*256) in our research, compressed them by using MLP for each compression process the number of neurons in the hidden layer was changing and calculating the compression ratio, mean square error and peak signal-to-noise ratio to compare the results to get the value of original image. The findings of the research was the desired results as the compression ratio was less than five and a few mean square error thus a large value of peak signal-to-noise ratio had been recorded.
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Wang, Yong, Guohua Geng, Pengbo Zhou, Qi Zhang, Zhan Li, and Ruihang Feng. "GC-MLP: Graph Convolution MLP for Point Cloud Analysis." Sensors 22, no. 23 (2022): 9488. http://dx.doi.org/10.3390/s22239488.
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With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. Unlike traditional local aggregation operations, the algorithm generates an adaptive kernel through the dynamic learning features of points, so that it can dynamically adapt to the structure of the object, i.e., the algorithm first adaptively assigns different weights to adjacent points according to the different relationships between the different points captured. Furthermore, local information interaction is then performed with the convolutional layers through a weight-sharing multilayer perceptron. Experimental results show that, under different task benchmark datasets (including ModelNet40 dataset, ShapeNet Part dataset, S3DIS dataset), our proposed algorithm achieves state-of-the-art for both point cloud classification and segmentation tasks.
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Pavić, Ivica, Frano Tomašević, and Ivana Damjanović. "Application of artificial neural networks for external network equivalent modeling." Journal of Energy - Energija 64, no. 1-4 (2022): 275–84. http://dx.doi.org/10.37798/2015641-4156.
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In this paper an artificial neural network (ANN) based methodology is proposed for determining an external network equivalent. The modified Newton-Raphson method with constant interchange of total active power between internal and external system is used for solving the load flow problem. A multilayer perceptron (MLP) with backpropagation training algorithm is applied for external network determination. The proposed methodology was tested with the IEEE 24-bus test network and simulation results show a very good performance of the ANN for external network modeling.
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Podsevalov, Artem G., Maxim A. Kiselev, and Andrey V. Ivanov. "Application of Multilayer Perceptron (MLP) neural network for detection and classification of cyber threats in network traffic." Digital technology security, no. 4 (December 26, 2024): 37–65. https://doi.org/10.17212/2782-2230-2024-4-37-65.
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This article examines the application of a multilayer perceptron (MLP) for network traffic classification aimed at detecting cyber threats. The model was trained on the NSL-KDD dataset, a standard dataset widely used in research for attack detection tasks. During the experiments, data preprocessing was conducted, including encoding of categorical features and class balancing using the SMOTE method to address the imbalance between normal and malicious traffic. The results demonstrated high classification accuracy of 96,64 %, even under noise conditions and 10-fold cross-validation, which confirms the reliability of the proposed approach. The article presents performance metrics such as accuracy, recall, and F1-score, which can serve as a foundation for further research and optimization of machine learning models to enhance network security.
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Soleymani Yazdi, M. Reza, and Michel Guillot. "Improving the Performance of Process Controllers Using a New Clustered Neural Network." Advanced Engineering Forum 1 (September 2011): 273–77. http://dx.doi.org/10.4028/www.scientific.net/aef.1.273.
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This paper presents first a newly developed clustered neural network, which incorporates self-organization capacity into the well-known common multilayer perceptron (MLP) architecture. With this addition, it is possible to reduce significantly overall memory degradation of the neuro-controller during on-line training. In the second part of the paper, this clustered multilayer perceptron (CMLP) network is applied and compared to the MLP through modeling and simulations of machining processes. Simulation results presented using machining data demonstrate that the CMLP possesses more powerful modeling capacity than the standard MLP, offers better adaptability to new operating conditions, and finally performs more reliably. During on-line training with machining data about 65% degradation of previously learned information can be observed in the MLP as opposed to only 11% for the CMLP. Finally, an adaptive control scheme intended for on-line optimization of the machining processes is presented. This scheme uses a feed forward CMLP inverse neuro-controller which learns off-line and on-line the relationships between process inputs and output under simulated perturbations (i.e., tool wear and non-homogeneous workpiece material properties). The first results using the CMLP inverse neuro-controller are promising
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Mohmad Hassim, Yana Mazwin, and Rozaida Ghazali. "Using Artificial Bee Colony to Improve Functional Link Neural Network Training." Applied Mechanics and Materials 263-266 (December 2012): 2102–8. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2102.
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Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.
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Mohanty, Sibani Priyadarshini, Syahrull Hi-Fi Syam Ahmad Jamil, Jailani Abdul Kadir, Mohd Salman Mohd Sabri, and Fakroul Ridzuan Hashim. "Cardiac Abnormality Prediction using Tansig Based Multilayer Perceptron." Jurnal Kejuruteraan si4, no. 2 (2021): 147–52. http://dx.doi.org/10.17576/jkukm-2021-si4(2)-22.
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An artificial neural network (ANN) is a network designed with adaptation to a computer system. The developed computer system will perform functions oriented to the way the brain works (neuron concept). This study is an extension to the study of the suitability of ANN to be applied in numbers of applications, especially in the field of medical engineering. ANN has been widely being used in medicine, ANN is widely applied in education, research, and even decision making. In this study, ANN will be trained for pre-testing to predict the cardiac abnormalities symptom based on selected reference parameters. This reference parameter is better known as the input parameter to the ANN to detect cardiac abnormalities, among which are the of the height of peak/wave (amplitude) and time occurrence of peak/wave (duration of time) extracted from the electrocardiogram (ECG) signal. A complete ECG complex contains a P peak, a QRS wave, and a T peak. For each P peak, QRS wave, and T peak, amplitude height and duration will be measured to serve as input parameters. This makes six parameters defined as inputs to the ANN. This study has used a Multilayer Perceptron (MLP) network as ANN structure by being trained using three different training algorithms namely Backpropagation (BP), Lavenberg Marquardt (LM) and Bayesian Regularization (BR). At the end of the study, it showed the MLP network which by BR training algorithm gave the highest accuracy prediction (94.04%), followed by LM (92.95%) and BP (88.77%). In this study all MLP networks were activated using the Tansig activation function.
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Dix, Jeff, Jeremy Holleman, and Benjamin J. Blalock. "Programmable Energy-Efficient Analog Multilayer Perceptron Architecture Suitable for Future Expansion to Hardware Accelerators." Journal of Low Power Electronics and Applications 13, no. 3 (2023): 47. http://dx.doi.org/10.3390/jlpea13030047.
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A programmable, energy-efficient analog hardware implementation of a multilayer perceptron (MLP) is presented featuring a highly programmable system that offers the user the capability to create an MLP neural network hardware design within the available framework. In addition to programmability, this implementation provides energy-efficient operation via analog/mixed-signal design. The configurable system is made up of 12 neurons and is fabricated in a standard 130 nm CMOS process occupying approximately 1 mm2 of on-chip area. The system architecture is analyzed in several different configurations with each achieving a power efficiency of greater than 1 tera-operations per watt. This work offers an energy-efficient and scalable alternative to digital configurable neural networks that can be built upon to create larger networks capable of standard machine learning applications, such as image and text classification. This research details a programmable hardware implementation of an MLP that achieves a peak power efficiency of 5.23 tera-operations per watt while consuming considerably less power than comparable digital and analog designs. This paper describes circuit elements that can readily be scaled up at the system level to create a larger neural network architecture capable of improved energy efficiency.
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PAULI, Suellen Teixeira Zavadzki de, Mariana KLEINA, and Wagner Hugo BONAT. "MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS: AN APPROACH FOR LEARNING THROUGH THE BAYESIAN FRAMEWORK." REVISTA BRASILEIRA DE BIOMETRIA 39, no. 1 (2021): 45–59. http://dx.doi.org/10.28951/rbb.v39i1.495.
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The machine learning area has recently gained prominence and articial neural networks are among the most popular techniques in this eld. Such techniques have the learning capacity that occurs during an iterative process of model tting. Multilayer perceptron (MLP) is one of the rst networks that emerged and, for thisarchitecture, backpropagation and its modications are widely used learning algorithms. In this article, the learning of the MLP neural network was approached from the Bayesian perspective by using Monte Carlo via Markov Chains (MCMC) simulations. The MLP architecture consists of the input, hidden and output layers. In the structure, there are several weights that connect each neuron in each layer. The input layer is composedof the covariates of the model. In the hidden layer there are activation functions. In the output layer, there are the result which is compared with the observed value and the loss function is calculated. We analyzed the network learning through simulated data of known weights in order to understand the estimation by the Bayesian method. Subsequently, we predicted the price of WTI oil and obtained a credibility interval for theforecasts. We provide an R implementation and the datasets as supplementary materials.
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Jia, Wendi, and Quanlong Chen. "Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network." Applied Sciences 14, no. 21 (2024): 9995. http://dx.doi.org/10.3390/app14219995.
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In the field of aeronautics, aircraft, as a critical aviation tool, exert a decisive influence on the structural integrity and safety of the entire system. Accurate prediction of the stress field distribution and variations within the aircraft structure is of great importance to ensuring its safety performance. To facilitate such predictions, a rapid assessment method for stress fields based on a multilayer perceptron (MLP) neural network is proposed. Compared to the traditional machine learning algorithm, the random forest algorithm, MLP demonstrates superior accuracy and computational efficiency in stress field prediction, particularly exhibiting enhanced adaptability when handling high-dimensional input data. This method is applied to predict stresses in the wing rib structure. By performing finite element meshing on the wing ribs, the angle of attack, inflow velocity, and node coordinates are utilized as input tensors for the model, enabling it to learn the stress distribution in the wing ribs. Additionally, a peak stress prediction model is separately established for regions experiencing peak stresses. The results indicate that the MAPE of the stress field prediction model is within 5%, with a coefficient of determination R2 exceeding 0.994. For the peak stress model, the MAPE is within 2%, with an R2 exceeding 0.995. This method offers faster computation and greater flexibility, presenting a novel approach for structural strength assessment.
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Chen, Je-Chian, and Yu-Min Wang. "Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network." Water 12, no. 5 (2020): 1281. http://dx.doi.org/10.3390/w12051281.
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The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as predictors, while the unmanned aerial vehicle (UAV) surveyed data of 2019 served as the respondent. The MLP was configured using five different activation functions with the aim of evaluating their significance. These functions were Identity, Tahn, Logistic, Exponential, and Sine Functions. The results have shown that the performance of an MLP model may be affected by the choice of an activation function. Logistic and the Tahn activation functions outperformed the other models, with Logistic performing best in three beaches and Tahn having the rest. These findings suggest that the application of machine learning to shoreline changes should be accompanied by an extensive evaluation of the different activation functions.
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Leite Coelho da Silva, Felipe, Kleyton da Costa, Paulo Canas Rodrigues, Rodrigo Salas, and Javier Linkolk López-Gonzales. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector." Energies 15, no. 2 (2022): 588. http://dx.doi.org/10.3390/en15020588.
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Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.
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Firsov, Nikita, Evgeny Myasnikov, Valeriy Lobanov, et al. "HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter." Sensors 24, no. 23 (2024): 7683. https://doi.org/10.3390/s24237683.
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In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov–Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neural networks with their KAN-based counterparts. Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). Experiments conducted using seven publicly available hyperspectral datasets demonstrated a substantial improvement in classification accuracy across all the networks. The best classification quality was achieved using a KAN-based transformer architecture.
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Alkhoja, Omnea. "Multilayer Perceptron Network to Detect Fraud in Digital Images." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 251–60. http://dx.doi.org/10.46649/fjiece.v3.2.17a.26.6.2024.
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The major challenge of data authenticity is how to check for image fraud, which createsa huge problemforthe credibility of visual media. In this paper, we proposea method to investigatethe performanceof a Multilayer Perceptron (MLP)to extract the fraud images,this network is a classof supervised Artificial Neural Network(ANN. Theproposal modelappliesMLP modelto allocateextracted image features in order to distinguishthembetween realand modifiedcontents. Theexaminedfeatures are includedwithinstatistical matrices, analysis of histogramspace, and possible inequality that may arise during modifications. The proposed MLP wastrained withdataset that contains both realand fraudulent images, thus allowing the modelto extractknowledge from the originalpatterns that differentiate between those two classes. The model's performance wasvalidated with severalmetrics, including accuracy,precision,and computational cost. Furthermore,this paper presentscomparisons against traditional methods that were examined in the procedure. The finding of this work enhances the model withimprovedimage fraud detection by showcasing the capabilities of MLPs within 162.59 seconds to 86% detection, while the base algorithm in 205.92 seconds succeeded in recognizing 82%.
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Sahu, Amaresh, and Sabyasachi Pattnaik. "Feature Selection Using Evolutionary Functional Link Neural Network for Classification." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 359. http://dx.doi.org/10.11591/ijaas.v6.i4.pp359-367.
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<p>Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm optimization (PSO) is used as learning tool for solving the problem of classification in data mining. EFLANN overcomes the non-linearity nature of problems by using the functionally expanded selected features, which is commonly encountered in single layer neural networks. The model is empirically compared to MLP, FLANN gradient descent learning algorithm, Radial Basis Function (RBF) and Hybrid Functional Link Neural Network (HFLANN) . The results proved that the proposed model outperforms the other models.</p>
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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-organizing map is carried out in Python with a wide range of modern standard tools, creation of a multilayer perceptron and a fuzzy network - using Neural Network Toolbox packages, and Fuzzy Logic Toolbox system MatLAB. On the created neural networks separately and on their combined variant researches of parameters of quality of definition of network attacks are carried out. It was determined that the error of the first kind was 11%, 4%, 10% and 0%, the error of the second kind - 7%, 6%, 9% and 6% on the fuzzy network, multilayer perceptron, self-organizing Kohonen map and their combined version, respectively, which proves the feasibility of using the combined option.
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Karlık, Bekir, and Kemal Yüksek. "Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System." Journal of Automated Methods and Management in Chemistry 2007 (2007): 1–6. http://dx.doi.org/10.1155/2007/38405.
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The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.
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Yan, Cong. "Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)." Computational Intelligence and Neuroscience 2021 (October 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/4133892.
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Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.
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Wang, Xuechun, and Vladimir L. Eliseev. "Methodology to improve the quality of neural network modeling of dynamic objects." Proceedings of Tomsk State University of Control Systems and Radioelectronics 27, no. 3 (2024): 92–99. https://doi.org/10.21293/1818-0442-2024-27-3-92-99.
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The problem of neural network modeling of nonlinear dynamic objects using recurrent neural networks is considered. An approach to improve the accuracy of modeling using a static neural network of the «multilayer perceptron» type, that processes correlation dependencies of a dynamic process and approximates the modeling error, is proposed. A technique for synthesis and application of the correlation neural network model CCF-MLP improving the quality of modeling of a conventional recurrent neural network, is formulated. Simulation experiments are carried out with a neural network recurrent network of the GRU type, that models the behavior of a nonlinear dynamic object, as well as GRU with the proposed CCF-MLP model. The improvement in the quality of modeling (RMSE, MAPE) is confirmed in the case of using CCFMLP both in the presence and absence of noise in the observed data. The practical applicability of the proposed method was tested on a real liquid level control system.