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Page 2423 of Psychological Abstracts Vol. 82, Issue 5 [page]

1995 Psychological Abstracts  
—Proposes a multilayer feedforward neural network and a design method that considers the distribution of given training patterns.  ...  (Chungnam National U, Dept of Mechatronics Eng, Taejon, South Korea) A novel design meth- od for multilayer feedforward neural networks. Neural Com- putation, 1994(Sep), Vol 6(5), 885-901.  ... 

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Jie Wu, Huihua Chen
2021 Computational Intelligence and Neuroscience  
This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural  ...  the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items.  ...  Acknowledgments is work in this article was supported by School of Anyang Institute of Technology.  ... 
doi:10.1155/2021/2861292 pmid:34899889 pmcid:PMC8660190 fatcat:go3tmfizbvfg7o4tpdebeo42vq

Assessing Seismic Hazard in Chile Using Deep Neural Networks [chapter]

Francisco Plaza, Rodrigo Salas, Orietta Nicolis
2019 Natural Hazards [Working Title]  
The results show a good performance of the deep neural network models for predicting future earthquake events.  ...  In that sense, this work consists in the implementation of a machine learning approach for assessing the earthquake risk in Chile, using information from 2012 to 2018.  ...  Acknowledgements The authors thank the National Research Center for Integrated National Disaster Management (CIGIDEN), CONICYT/FONDAP/15110017 (Chile) and CONICYT PFCHA/DOCTORADO BECAS CHILE/2018 -21182037  ... 
doi:10.5772/intechopen.83403 fatcat:o3dpmefqsbhmngyaqjaopcovi4

Assessment of Optimum Neural Network Architecture in Forecasting and Mining Carbon Emissions

Poornashankar, Vrushsen P. Pawar
2011 International Journal of Computer and Electrical Engineering  
Various experiments are conducted to justify the swift, high performing, accurate and adaptive network amongst Simple Feedforward(FF), Multilayer Perceptron (MLP) with Backpropagation and Kohonen's Self-Organizing  ...  This paper presents an approach to determine the best performing Artificial Neural Network (ANN) algorithm for the forecast of carbon emissions from fossil fuels and flares and projects the emission rate  ...  Generalized Feedforward (FF) networks, Multilayer perceptron (MLP) networks and Self Organized Map (SOM) architectures are selected in this research, as they are efficient and appropriate for non-linear  ... 
doi:10.7763/ijcee.2011.v3.317 fatcat:m6wk4hmorfbn5cfjrcefetsg5m

Trainable neural network for mechanically flexible systems based on nonlinear filtering

Hsin-Tan Chiu, Sabri Cetinkunt
1995 Journal of Guidance Control and Dynamics  
Neural Network Controller Architecture The neural network controller architecture is depicted in Fig. 2, which is a feedforward multilayer network.’  ...  A well-known training method for multilayer feedforward neu- ral networks is the error back propagation algorithm.’  ... 
doi:10.2514/3.21415 fatcat:6ioov3oepjfenjo7zoohl4gdda

Digit and Command Interpretation for Electronic Book Using Neural Network and Genetic Algorithm

H.K. Lam, F.H.F. Leung
2004 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
A digit-and-command interpreter constructed by the modified neural networks is proposed to recognize handwritten digits and commands.  ...  This paper presents the interpretation of digits and commands using a modified neural network and the genetic algorithm.  ...  A self-organizing map approach can also be found in [21] , of which the self-organizing map model were used to implement a modular classification system.  ... 
doi:10.1109/tsmcb.2004.834432 pmid:15619928 fatcat:sipvt5yp2retzbrigoog447pom

Neural networks as a method for elucidating structure–property relationships for organic compounds

Nataliya M Halberstam, Igor I Baskin, Vladimir A Palyulin, Nikolai S Zefirov
2003 Russian Chemical Reviews  
The published data devoted to the use of the neural The published data devoted to the use of the neural network approach in the simulation of structure ± property rela-network approach in the simulation  ...  The basic princi-tionships for organic compounds are reviewed.  ...  of organic compounds), multilayered feedforward backpropagation neural networks come first in popularity.  ... 
doi:10.1070/rc2003v072n07abeh000754 fatcat:lmunwdr665enhjei4nnzqq5g4a

A Review on Artificial Neural Networks and its' Applicability

Mustafa Nizamul Aziz
2020 Bangladesh Journal of Multidisciplinary Scientific Research  
ANN tries to emulate the neural structure of the brain, which consists of several thousand cells, neuron, which is interconnected in a large network.  ...  The field of artificial neural networks (ANN) started from humble beginnings in the 1950s but got attention in the 1980s.  ...  Often the feedforward approach, where the output of a node in one layer is not connected to the input of a node in a previous layer nor the same layer, but only to nodes in subsequent layers, is used on  ... 
doi:10.46281/bjmsr.v2i1.609 fatcat:7sa2llvbqnds5eqeymxb7ptyuy

Feature space mapping as a universal adaptive system

Włodzisław Duch, Geerd H.F. Diercksen
1995 Computer Physics Communications  
In comparison with the neural networks that use delocalized transfer functions this approach allows for full control of the basins of attractors of all stationary points.  ...  Quantization (LVQ) and on self-organizing mappings (SOM) [8] [9] [10] .  ...  Acknowledgments W.D. gratefully acknowledges support of the Max-Planck Institut für Astrophysik during his visits in Garching.  ... 
doi:10.1016/0010-4655(95)00023-9 fatcat:6alg6zcwp5d4paevvfv2y25ghq

Neural-Network-Based Models of 3-D Objects for Virtualized Reality: A Comparative Study

A.-M. Cretu, E.M. Petriu, G.G. Patry
2006 IEEE Transactions on Instrumentation and Measurement  
using either the self-organizing map (SOM) or the neural gas network.  ...  Starting from a pointcloud that embeds the shape of the object to be modeled, a volumetric representation is obtained using a multilayer feedforward neural network (MLFFNN) or a surface representation  ...  This paper discusses two categories of neural-network (NN)based 3-D object representation, namely 1) a multilayer feedforward neural network (MLFFNN) architecture and 2) two self-organizing architectures  ... 
doi:10.1109/tim.2005.860862 fatcat:je6ylcosengazjxwwdyw2vxryu

Applications of Artificial Neural Network in Textiles

Neha Chauhan, Nirmal Yadav, Nisha Arya
2018 International Journal of Current Microbiology and Applied Sciences  
The artificial neural network (ANN) is increasingly used as a powerful tool for many real world problems.  ...  The power of neural networks lies in their ability to represent complex relationships and learn them directly from the data being modeled.  ...  Single layer feedforward networks, Multilayer feedforward networks and Recurrent networks as shown in Fig. 1, 2 and 3 respectively.  ... 
doi:10.20546/ijcmas.2018.704.356 fatcat:ethphxg435fn7d4sjdmvg4fd6q

Basic genetic-algorithm-neural-network (GANN) pattern with a self-organizing security example

David Streisand, Rick Dove
2012 2012 IEEE International Carnahan Conference on Security Technology (ICCST)  
Another such pattern is seen in artificial neural networks. Combining the two into a Genetic Algorithm augmented Neural Network, often called GANN, has considerable recent history in the literature.  ...  The anti-system adversarial community is characterized as a self-organizing system-of-systems, noted collectively for its leadership in rapid evolution and innovative advancement; widening the gap between  ...  ACKNOWLEDGEMENTS Some of the work covered in this paper was funded by the Department of Homeland Security under contract D10PC20039.  ... 
doi:10.1109/ccst.2012.6393578 dblp:conf/iccst/StreisandD12 fatcat:z5pcmzwvfzg27i45syjykg563q

Self-Organizing Learning Array

J.A. Starzyk, Z. Zhu, T.-H. Liu
2005 IEEE Transactions on Neural Networks  
By choosing connections for each neuron, the system sets up its wiring and completes its self-organization.  ...  A new machine learning concept-self-organizing learning array (SOLAR)-is presented. It is a sparsely connected, information theory-based learning machine, with a multilayer structure.  ...  For instance, a CMAC network based self-organizing classifier design has recently been reported in [11] .  ... 
doi:10.1109/tnn.2004.842362 pmid:15787142 fatcat:gufohlv44vacno5w7ycqhiyhqy

Self-configuring Two Types of Neural Networks by MPCA

uliana A. Anochi, Haroldo F. Campos Velho, Helaine C.M. Furtado, Eduardo F.P. Luz
2015 Journal of Mechanics Engineering and Automation  
In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here.  ...  An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application.  ...  for research support.  ... 
doi:10.17265/2159-5275/2015.02.008 fatcat:mndiy2huqrh6hnocepvmdwu5fu

A recurrent self-organizing neural fuzzy inference network

Chia-Feng Juang, Chin-Teng Lin
1999 IEEE Transactions on Neural Networks  
A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper.  ...  The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network.  ...  The recurrent neural fuzzy network proposed in this paper is called recurrent self-organizing neural fuzzy inference network (RSONFIN).  ... 
doi:10.1109/72.774232 pmid:18252581 fatcat:2dymxeot5bdhzbhzdv2ublzjui
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