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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/874

Title: Adaptive nonlinear system identification using Comprehensive Learning PSO
Authors: Katari, V
Malireddi, S
Bendapudi, S K S
Panda, G
Keywords: adaptive systems
genetic algorithms
identification
least mean squares methods
nonlinear control systems
particle swarm optimisation
Issue Date: 2008
Publisher: IEEE
Citation: 3rd International Symposium on Communications, Control and Signal Processing, 2008. ISCCSP, St. Julians, March 12-14, 2008.
Abstract: In this paper we introduce the Comprehensive Learning Particle Swarm Optimization (CLEPSO) technique for identification of nonlinear systems. System identification in noisy environment has been a matter of concern for researchers in control theory for nonlinear analysis and optimization. In the recent past the Least Mean Square Algorithm (LMS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. have been employed for developing mathematical archetype of an anonymous system. LMS performs inversely with nonlinearity. Although PSO performs better than GA in terms of convergence rate, it suffers from premature convergence. To alleviate the problem we propose a novel CLEPSO technique for updating the parameters of the Functional Link Artificial Neural Network (FLANN) model. The CLEPSO is a variant of PSO which ascertains the convergence of the model parameters to the global optimum with a faster speed and better accuracy. Comprehensive computer simulations corroborate that CLEPS...
URI: http://10.1109/ISCCSP.2008.4537265
http://hdl.handle.net/2080/874
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