Relevant bibliographies by topics / Multilayer perceptron (MLP) neural network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Multilayer perceptron (MLP) neural network'

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

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Journal articles on the topic "Multilayer perceptron (MLP) neural network"

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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
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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 cla
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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 neura
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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 consta
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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 o
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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
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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
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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 Pre
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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 ne
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Dissertations / Theses on the topic "Multilayer perceptron (MLP) neural network"

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Steinholtz, Tim. "Skip connection in a MLP network for Parkinson’s classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303130.

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In this thesis, two different architecture designs of a Multi-Layer Perceptron network have been implemented. One architecture being an ordinary MLP, and in the other adding DenseNet inspired skip connections to an MLP architecture. The models were used and evaluated on the classification task, where the goal was to classify if subjects were diagnosed with Parkinson’s disease or not based on vocal features. The models were trained on an openly available dataset for Parkinson’s classification and evaluated on a hold-out set from this dataset and on two datasets recorded in another sound recordi
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Gaspar, Thiago Lombardi. "Reconhecimento de faces humanas usando redes neurais MLP." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-27042006-231620/.

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O objetivo deste trabalho foi desenvolver um algoritmo baseado em redes neurais para o reconhecimento facial. O algoritmo contém dois módulos principais, um módulo para a extração de características e um módulo para o reconhecimento facial, sendo aplicado sobre imagens digitais nas quais a face foi previamente detectada. O método utilizado para a extração de características baseia-se na aplicação de assinaturas horizontais e verticais para localizar os componentes faciais (olhos e nariz) e definir a posição desses componentes. Como entrada foram utilizadas imagens faciais de três bancos distin
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Ferro, Luciano [UNESP]. "Aplicação da rede neural MLP (Multilayer Perceptron) em indústria de pisos e revestimentos do Pólo Cerâmico de Santa Gertrudes - SP." Universidade Estadual Paulista (UNESP), 2013. http://hdl.handle.net/11449/102925.

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Made available in DSpace on 2014-06-11T19:32:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-04-25Bitstream added on 2014-06-13T19:21:52Z : No. of bitstreams: 1 ferro_l_dr_rcla.pdf: 507040 bytes, checksum: 8569d113b387622fd192e005a1bbf02b (MD5)<br>As Redes Neurais Artificiais se constituem numa alternativa à computação programada tradicional e foram aplicadas em quase todos os ramos do conhecimento humano. Em Geotecnologia, no entanto, ainda são escassas as aplicações de maneira que, com este trabalho, procura-se mostrar que elas também podem ser aplicadas em indústrias de pisos e
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Ferro, Luciano. "Aplicação da rede neural MLP (Multilayer Perceptron) em indústria de pisos e revestimentos do Pólo Cerâmico de Santa Gertrudes - SP /." Rio Claro, 2013. http://hdl.handle.net/11449/102925.

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Orientador: José Ricardo Sturaro<br>Banca: Paulo Milton Barbosa Landim<br>Banca: Ricardo Egydio de Carvalho<br>Banca: Alessandro Firmiano de Jesus<br>Banca: Alexandre Campane Vidal<br>Resumo: As Redes Neurais Artificiais se constituem numa alternativa à computação programada tradicional e foram aplicadas em quase todos os ramos do conhecimento humano. Em Geotecnologia, no entanto, ainda são escassas as aplicações de maneira que, com este trabalho, procura-se mostrar que elas também podem ser aplicadas em indústrias de pisos e revestimentos cerâmicos do Pólo Cerâmico de Santa Gertrudes, Estado
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Goosen, Johannes Christiaan. "Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/5552.

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In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation sa
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Liberatore, Lorenzo. "Introduction to geometric deep learning and graph neural networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25339/.

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This thesis proposes an introduction to the fundamental concepts of supervised deep learning. Starting from Rosemblatt's Perceptron we will discuss the architectures that, in recent years, have revolutioned the world of deep learning: graph neural networks, which led to the formulation of geometric deep learning. We will then give a simple example of graph neural network, discussing the code that composes it and then test our architecture on the MNISTSuperpixels dataset, which is a variation of the benchmark dataset MNIST.
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Gouveia, Wellington da Rocha. "Detecção de faces humanas em imagens coloridas utilizando redes neurais artificiais." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-11032010-160048/.

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A tarefa de encontrar faces em imagens é extremamente complexa, pois pode ocorrer variação de luminosidade, fundos extremamente complexos e objetos que podem se sobrepor parcialmente à face que será localizada, entre outros problemas. Com o avanço na área de visão computacional técnicas mais recentes de processamento de imagens e inteligência artificial têm sido combinadas para desenvolver algoritmos mais eficientes para a tarefa de detecção de faces. Este trabalho apresenta uma metodologia de visão computacional que utiliza redes neurais MLP (Perceptron Multicamadas) para segmentar a cor da p
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Ridhagen, Markus, and Petter Lind. "A comparative study of Neural Network Forecasting models on the M4 competition data." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445568.

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The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time fre
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Gao, Zhenning. "Parallel and Distributed Implementation of A Multilayer Perceptron Neural Network on A Wireless Sensor Network." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1383764269.

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Midhall, Ruben, and Amir Parmbäck. "Utvärdering av Multilayer Perceptron modeller för underlagsdetektering." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43469.

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Antalet enheter som är uppkopplade till internet, Internet of Things (IoT), ökar hela tiden. År 2035 beräknas det finnas 1000 miljarder Internet of Things-enheter. Samtidigt som antalet enheter ökar, ökar belastningen på internet-nätverken som enheterna är uppkopplade till. Internet of Things-enheterna som finns i vår omgivning samlar in data som beskriver den fysiska tillvaron och skickas till molnet för beräkning. För att hantera belastningen på internet-nätverket flyttas beräkningarna på datan till IoT-enheten, istället för att skicka datan till molnet. Detta kallas för edge computing. IoT-
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Book chapters on the topic "Multilayer perceptron (MLP) neural network"

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Mohammadazadeh, Ardahir, Mohammad Hosein Sabzalian, Oscar Castillo, Rathinasamy Sakthivel, Fayez F. M. El-Sousy, and Saleh Mobayen. "Multilayer Perceptron (MLP) Neural Networks." In Synthesis Lectures on Intelligent Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14571-1_2.

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Westby, Isaac, Hakduran Koc, Jiang Lu, and Xiaokun Yang. "A Design on Multilayer Perceptron (MLP) Neural Network for Digit Recognition." In Transactions on Computational Science and Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_53.

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Westby, Isaac, Hakduran Koc, Jiang Lu, and Xiaokun Yang. "A Design on Multilayer Perceptron (MLP) Neural Network for Digit Recognition." In Transactions on Computational Science and Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_53.

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Clark, Jonathan Y., and Kevin Warwick. "Artificial Keys for Botanical Identification using a Multilayer Perceptron Neural Network (MLP)." In Artificial Intelligence for Biology and Agriculture. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5048-4_5.

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Shyamsunder, Metuku, and Kakarla Subba Rao. "Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5550-1_23.

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Tashan, Tariq, and Tony Allen. "Two stage speaker verification using Self Organising Map and Multilayer Perceptron Neural Network." In Research and Development in Intelligent Systems XXVIII. Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2318-7_8.

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Hao, Jianbin, Shaohua Tan, and Joos Vandewalle. "A Geometric Approach to the Structural Synthesis of Multilayer Perceptron Neural Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_120.

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Shivappriya, S. N., Rajaguru Harikumar, Krishnamoorthi Maheswari, and Ramasamy Dhivya Praba. "Heart Disease Classification Using Multi-Layer Perceptron (MLP) Neural Network." In Sustainable Digital Technologies for Smart Cities. CRC Press, 2023. http://dx.doi.org/10.1201/9781003307716-14.

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Woo, Dong-Min, and Dong-Chul Park. "Application of MultiLayer Perceptron Type Neural Network to Camera Calibration." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_15.

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Shyamala Devi, M., A. Peter Soosai Anandaraj, K. Venkata Thanooj, P. V. Sandeep Guptha, and A. Jayanth Reddy. "Multilayer Perceptron Neural Network Supervised Learning Based Solar Radiation Prediction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2281-7_58.

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Conference papers on the topic "Multilayer perceptron (MLP) neural network"

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Pradhan, Dr Moumita. "PredParkinson-MLP: Parkinson Disease Prediction using Multi Layer Perceptron Neural Network." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968652.

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Solemani, Mohammad R., and Michel Guillot. "Improving the Performance of Process Controllers Using a New Clustered Neural Network." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2368.

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Abstract 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 pow
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Ghorbanian, Kaveh, and Mohammad Gholamrezaei. "Axial Compressor Performance Map Prediction Using Artificial Neural Network." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27165.

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The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural network such as multilayer perceptron network, radial basis function network, general regression neural network, and a rotated general regression neural network proposed by the authors are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data, it is however limited to curve fitting app
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Harun, N., S. S. Dlay, and W. L. Woo. "Performance of keystroke biometrics authentication system using Multilayer Perceptron neural network (MLP NN)." In 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/csndsp16145.2010.5580334.

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Aravind, Aditya, Fahad M. Mujawar, Rajat Kumar Sinha, and Kaustav Bhowmick. "Use of Multilayer Perceptron Classifier for Determination of Single Mode Operation in Rib Waveguides." In Frontiers in Optics. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/fio.2023.jm7a.16.

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The present work reports the performance of a MLP neural network architecture, classifying single-mode operation for Rib waveguides with varying refractive indices, wavelengths, and geometrical parameters, with an accuracy of ~ 90% and faster computation.
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Li, De, Honghai Wang, and Zhengying Li. "Accurate and Fast Wavelength Demodulation for Fbg Reflected Spectrum Using Multilayer Perceptron (Mlp) Neural Network." In 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2020. http://dx.doi.org/10.1109/icmtma50254.2020.00066.

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Li, J. W., Y. C. Manie, P. H. Chiu, A. M. Dehnaw, and P. C. Peng. "Optical Comb Generator-based Microwave Photonic Filter Performance Improvement Using Multilayer Perceptron (MLP) Neural Network." In 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2021. http://dx.doi.org/10.1109/icce-tw52618.2021.9603173.

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Rywik, Marcin, Axel Zimmermann, Alexander J. Eder, Edoardo Scoletta, and Wolfgang Polifke. "Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron - Convolution Autoencoder Network." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-102543.

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Abstract This work presents a multilayer perceptron-convolutional autoencoder (MLP-CAE) neural network model, which accurately predicts the two-dimensional flame field dynamics of an acoustically excited premixed laminar flame. The obtained architecture maps the acoustic perturbation time series to a spatially distributed heat release rate field, capturing the flame lengths and shapes. This extends to previous neural network models, which predicted only the field-integrated value of the heat release rate. The MLP-CAE comprises two sub-models: a fully connected MLP and a CAE. The key idea behin
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Chen, Shu, Peng Ding, Shuowen Hu, et al. "Optimization of Core Parameters Based on Artificial Neural Network Surrogate Model." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-90511.

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Abstract The methodology of artificial intelligence (AI), particularly artificial neural network (ANN), would be in favor of nuclear energy system development. These ANN simulators may provide more efficient means than the traditional nuclear design codes, especially for the design of the key parameters, such as core geometry and layout, material composition. In this paper, a neutronics calculation code SARAX and the corresponding multilayer perceptron (MLP) surrogate model were used as simulators for core parameters optimization of a reference lead based fast reactor. The pellet radius, enric
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Coelho, Bruno França, and João Viana Fonseca Neto. "Extended Kalman Filter Enhanced by Neural Network to Solve the SLAM Problem." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-59.

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This work presents a way for online estimation of the location and mapping of a non-holonomic robot by means of an algorithm that uses EKF and in the output of this algorithm, a multilayer perceptron neural network (MLP) has been added that aims to improve the estimation of the robot pose in an unfamiliar environment. The effectiveness was proven through the comparison between the EKF-SLAM and the EKFMLP-SLAM, where it was evidenced a significant improvement in relation to the location of the poses of the robot.
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Reports on the topic "Multilayer perceptron (MLP) neural network"

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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