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

Content Provider	Springer Nature Link
Author	Sheikhan, Mansour Sha’bani, Amir Ali
Copyright Year	2012
Abstract	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.
Starting Page	519
Ending Page	530
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-04-25
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fault diagnosis Modular neural model Analog circuits PSO algorithm OWO-HWO algorithm Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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