Relevant bibliographies by topics / Multi-layer perceptron networks (MLPNs)

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Academic literature on the topic 'Multi-layer perceptron networks (MLPNs)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Multi-layer perceptron networks (MLPNs)"

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Przybył, Krzysztof, Krzysztof Koszela, Franciszek Adamski, Katarzyna Samborska, Katarzyna Walkowiak, and Mariusz Polarczyk. "Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders." Sensors 21, no. 17 (2021): 5823. http://dx.doi.org/10.3390/s21175823.

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In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution N
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Rohman, Budiman Putra Asmaur, and Dayat Kurniawan. "Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number." Jurnal Elektronika dan Telekomunikasi 17, no. 1 (2017): 19. http://dx.doi.org/10.14203/jet.v17.19-24.

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Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the ra
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Bologna, Guido. "A Simple Convolutional Neural Network with Rule Extraction." Applied Sciences 9, no. 12 (2019): 2411. http://dx.doi.org/10.3390/app9122411.

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Classification responses provided by Multi Layer Perceptrons (MLPs) can be explained by means of propositional rules. So far, many rule extraction techniques have been proposed for shallow MLPs, but not for Convolutional Neural Networks (CNNs). To fill this gap, this work presents a new rule extraction method applied to a typical CNN architecture used in Sentiment Analysis (SA). We focus on the textual data on which the CNN is trained with “tweets” of movie reviews. Its architecture includes an input layer representing words by “word embeddings”, a convolutional layer, a max-pooling layer, fol
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CAIRNS, GRAHAM, and LIONEL TARASSENKO. "PERTURBATION TECHNIQUES FOR ON-CHIP LEARNING WITH ANALOGUE VLSI MLPs." Journal of Circuits, Systems and Computers 06, no. 02 (1996): 93–113. http://dx.doi.org/10.1142/s0218126696000108.

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Microelectronic neural network technology has become sufficiently mature over the past few years that reliable performance can now be obtained from VLSI circuits under carefully controlled conditions (see Refs. 8 or 13 for example). The use of analogue VLSI allows low power, area efficient hardware realisations which can perform the computationally intensive feed-forward operation of neural networks at high speed, making real-time applications possible. In this paper we focus on important issues for the successful operation and implementation of on-chip learning with such analogue VLSI neural
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Suprapto, Suprapto, and Edy Riyanto. "Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks." Indonesian Journal of Science and Technology 5, no. 3 (2020): 382–94. http://dx.doi.org/10.17509/ijost.v5i3.24991.

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This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control
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Geng, Chao, Qingji Sun, and Shigetoshi Nakatake. "Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks." Sensors 20, no. 15 (2020): 4222. http://dx.doi.org/10.3390/s20154222.

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Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 μm/±2.5 V complementary metal oxide semiconductor (CMOS) process, which are comprised of digital-to-analog converter (DAC)-based multipliers and phase shifters. The results from the measurement convinces us that our implementation attains the correct function and good performance. Furthermore, we propose
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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik, and Aissa Ameur. "Induction machine stator short-circuit fault detection using support vector machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (2021): 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.

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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, na
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Loukeris, Nikolaos, and Iordanis Eleftheriadis. "Further Higher Moments in Portfolio Selection andA PrioriDetection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron." International Journal of Finance & Economics 20, no. 4 (2015): 341–61. http://dx.doi.org/10.1002/ijfe.1521.

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Przybył, Krzysztof, Jolanta Wawrzyniak, Krzysztof Koszela, Franciszek Adamski, and Marzena Gawrysiak-Witulska. "Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed." Sensors 20, no. 24 (2020): 7305. http://dx.doi.org/10.3390/s20247305.

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This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Netwo
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He, Hao, Jiaxiang Zhao, and Guiling Sun. "Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information." Entropy 21, no. 7 (2019): 635. http://dx.doi.org/10.3390/e21070635.

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Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We
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Dissertations / Theses on the topic "Multi-layer perceptron networks (MLPNs)"

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Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.

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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-lay
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Zheng, Gonghui. "Design and evaluation of a multi-output-layer perceptron." Thesis, University of Ulster, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338195.

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Vural, Hulya. "Comparison Of Rough Multi Layer Perceptron And Rough Radial Basis Function Networks Using Fuzzy Attributes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605293/index.pdf.

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The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquo<br>low&rdquo<br>, &ldquo<br>medium&rdquo<br>and &ldquo<br>high&rdquo<br>. In the roug
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Dlugosz, Stephan. "Multi-layer perceptron networks for ordinal data analysis : order independent online learning by sequential estimation /." Berlin : Logos, 2008. http://d-nb.info/990567311/04.

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McGarry, Kenneth J. "Rule extraction and knowledge transfer from radial basis function neural networks." Thesis, University of Sunderland, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391744.

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Valmiki, Geetha Charan, and Akhil Santosh Tirupathi. "Performance Analysis Between Combinations of Optimization Algorithms and Activation Functions used in Multi-Layer Perceptron Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20204.

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Background:- Artificial Neural networks are motivated from biological nervous system and can be used for classification and forecasting the data. Each neural node contains activation function could be used for solving non-linear problems and optimization function to minimize the loss and give more accurate results. Neural networks are bustling in the field of machine learning, which inspired this study to analyse the performance variation based on the use of different combinations of the activation functions and optimization algorithms in terms of accuracy results and metrics recall and impact
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Andrade, Kléber de Oliveira. "Sistema neural reativo para o estacionamento paralelo com uma única manobra em veículos de passeio." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-21112011-131734/.

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Graças aos avanços tecnológicos nas áreas da computação, eletrônica embarcada e mecatrônica a robótica está cada vez mais presente no cotidiano da pessoas. Nessas últimas décadas, uma infinidade de ferramentas e métodos foram desenvolvidos no campo da Robótica Móvel. Um exemplo disso são os sistemas inteligentes embarcados nos veículos de passeio. Tais sistemas auxiliam na condução através de sensores que recebem informações do ambiente e algoritmos que analisam os dados e tomam decisões para realizar uma determinada tarefa, como por exemplo estacionar um carro. Este trabalho tem por objetivo
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Cherif, Aymen. "Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4003/document.

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La prévision des séries temporelles est un problème qui est traité depuis de nombreuses années. On y trouve des applications dans différents domaines tels que : la finance, la médecine, le transport, etc. Dans cette thèse, on s’est intéressé aux méthodes issues de l’apprentissage artificiel : les réseaux de neurones et les SVM. On s’est également intéressé à l’intérêt des méta-méthodes pour améliorer les performances des prédicteurs, notamment l’approche locale. Dans une optique de diviser pour régner, les approches locales effectuent le clustering des données avant d’affecter les prédicteurs
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Oliveira, Rogério Campos de. "Aplicação de máquinas de comitê de redes neurais artificiais na solução de um problema inverso em transferência radiativa." Universidade do Estado do Rio de Janeiro, 2010. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=1732.

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Este trabalho fundamenta-se no conceito de máquina de comitê de redes neurais artificiais e tem por objetivo resolver o problema inverso de transferência radiativa em um meio unidimensional, homogêneo, absorvedor e espalhador isotrópico. A máquina de comitê de redes neurais artificiais agrega e combina o conhecimento adquirido por um certo número de especialistas aqui representados, individualmente, por cada uma das redes neurais artificiais (RNA) que compõem a máquina de comitê de redes neurais artificiais. O objetivo é atingir um resultado final melhor do que o obtido por qualquer rede neura
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Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga
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Books on the topic "Multi-layer perceptron networks (MLPNs)"

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Dissertation: Autonomous Construction of Multi Layer Perceptron Neural Networks. Storming Media, 1997.

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Book chapters on the topic "Multi-layer perceptron networks (MLPNs)"

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Shepherd, Adrian J. "Multi-Layer Perceptron Training." In Second-Order Methods for Neural Networks. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2_1.

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Multi Layer Perceptron Networks." In Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_2.

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Pérez-Miñana, Elena, Peter Ross, and John Hallam. "Multi-layer perceptron design using Delaunay triangulations." In Fuzzy Logic, Neural Networks, and Evolutionary Computation. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_22.

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Khoi, Duong Dang, and Yuji Murayama. "Multi-layer Perceptron Neural Networks in Geospatial Analysis." In Progress in Geospatial Analysis. Springer Japan, 2012. http://dx.doi.org/10.1007/978-4-431-54000-7_9.

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Lang, Bernhard. "Monotonic Multi-layer Perceptron Networks as Universal Approximators." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_6.

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Sureddy, Sneha, and Jeena Jacob. "Multi-features Based Multi-layer Perceptron for Facial Expression Recognition System." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84760-9_19.

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Suksmono, Andriyan Bayu, and Akira Hirose. "Adaptive Beamforming by Using Complex-Valued Multi Layer Perceptron." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_114.

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Trunfio, Giuseppe A. "Enhancing Cellular Automata by an Embedded Generalized Multi-layer Perceptron." In Artificial Neural Networks: Biological Inspirations – ICANN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_54.

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Eleuteri, Antonio, Roberto Tagliaferri, and Leopoldo Milano. "Divergence Projections for Variable Selection in Multi–layer Perceptron Networks." In Neural Nets. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_32.

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Mahesh, Vijayalakshmi G. V., Alex Noel Joseph Raj, and P. Arulmozhivarman. "Thermal IR Face Recognition Using Zernike Moments and Multi Layer Perceptron Neural Network (MLPNN) Classifier." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60618-7_21.

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Conference papers on the topic "Multi-layer perceptron networks (MLPNs)"

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Motato, Eliot, and Clark Radcliffe. "Recursive Assembly of Multi-Layer Perceptron Neural Networks." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5997.

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The objective of this paper is to present a methodology to modularly connect Multi-Layer Perceptron (MLP) neural network models describing static port-based physical behavior. The MLP considered in this work are characterized for an standard format with a single hidden layer with sigmoidal activation functions. Since every port is defined by an input-output pair, the number of outputs of the proposed neural network format is equal to the number of its inputs. This work extends the Model Assembly Method (MAM) used to connect transfer function models and Volterra models to multi-layer perceptron
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Mirjalili, Seyedali, and Ali Safa Sadiq. "Magnetic Optimization Algorithm for training Multi Layer Perceptron." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014845.

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Karami, A. R., M. Ahmadian Attari, and H. Tavakoli. "Multi Layer Perceptron Neural Networks Decoder for LDPC Codes." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5303382.

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Ikuta, Chihiro, Yoko Uwate, and Yoshifumi Nishio. "Investigation of four-layer multi-layer perceptron with glia connections of hidden-layer neurons." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706921.

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Wang, Zihan, Zhaochun Ren, Chunyu He, Peng Zhang, and Yue Hu. "Robust Embedding with Multi-Level Structures for Link Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/728.

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Knowledge Graph (KG) embedding has become crucial for the task of link prediction. Recent work applies encoder-decoder models to tackle this problem, where an encoder is formulated as a graph neural network (GNN) and a decoder is represented by an embedding method. These approaches enforce embedding techniques with structure information. Unfortunately, existing GNN-based frameworks still confront 3 severe problems: low representational power, stacking in a flat way, and poor robustness to noise. In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above chal
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Van Efferen, Lennart, and Amr M. T. Ali-Eldin. "A multi-layer perceptron approach for flow-based anomaly detection." In 2017 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2017. http://dx.doi.org/10.1109/isncc.2017.8072036.

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Msiza, Ishmael S., Fulufhelo V. Nelwamondo, and Tshilidzi Marwala. "Water Demand Forecasting Using Multi-layer Perceptron and Radial Basis Functions." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4370923.

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Ngwar, Melin, and Jim Wight. "A fully integrated analog neuron for dynamic multi-layer perceptron networks." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280448.

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Toderean, Roxana. "Classification of Sensorimotor Rhythms Based on Multi-layer Perceptron Neural Networks." In 2020 International Conference on Development and Application Systems (DAS). IEEE, 2020. http://dx.doi.org/10.1109/das49615.2020.9108910.

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Alsmadi, Mutasem khalil, Khairuddin Bin Omar, Shahrul Azman Noah, and Ibrahim Almarashdah. "Performance Comparison of Multi-layer Perceptron (Back Propagation, Delta Rule and Perceptron) algorithms in Neural Networks." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4809024.

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