Treffer: PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Title:
PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits
Authors:
SHEIKHAN, Mansour, AMIR ALI SHA'BANI
Source:
Neural computing & applications (Print). 23(2):519-530
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 110 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Calcul neuronal, Neural computation, computación neuronal, Circuit analogique, Analog circuit, Circuito analógico, Classificateur, Classifier, Clasificador, Diagnostic, Diagnosis, Diagnóstico, Faille, Fault, Quebrado, Méthode optimisation, Optimization method, Método optimización, Noeud structure, Nodes, Nudo estructura, Optimisation, Optimization, Optimización, Perceptron multicouche, Multilayer perceptrons, Réseau neuronal, Neural network, Red neuronal, Résultat expérimental, Experimental result, Resultado experimental, Structure réseau, Network structure, Théorie circuit, Circuit theory, Teoría circuito, Vitesse convergence, Convergence speed, Velocidad convergencia, 49XX, 62M45, 62P20, 65K10, 65Kxx, Circuit neuronal, Neural circuit, Couche cachée, Modèle réseau neuronal, Training algorithm, Analog circuits, Fault diagnosis, Modular neural model, OWO-HWO algorithm, PSO algorithm
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, Faculty of Engineering, Islamic Azad University, South Tehran Branch, P.O. Box: 11365-4435, Tehran, Iran, Islamic Republic of
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27658253
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.27658253
Database:
PASCAL Archive

Weitere Informationen

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization-hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization-backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.