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

• Published in: Neural computing & applications Vol. 23; no. 2; pp. 519 - 530
• Main Authors: Sheikhan, Mansour, Sha’bani, Amir Ali
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2013; Springer
• Subjects: Applied sciences; Artificial Intelligence; Calculus of variations and optimal control; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Learning and adaptive systems; Mathematical analysis; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in mathematical programming, optimization and calculus of variations; Numerical methods in optimization and calculus of variations; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics; Fault diagnosis; Analog circuits; OWO-HWO algorithm; Modular neural model; PSO algorithm; Analog circuit; Network structure; Neural computation; Optimization method; Fault; Classifier; Neural network; Nodes; Optimization; Convergence speed; Backpropagation algorithm; Experimental result; Multilayer perceptrons; Neural circuit; Diagnosis; Circuit theory
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-0947-9

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.