Nonlinear system identification using memetic differential evolution trained neural networks
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2011
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Abstract
Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.
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Keywords
Bench-mark problems, BP algorithm, Correlation analysis, Differential evolution, Differential evolution algorithms, Evolutionary computation, Experimental aerodynamics, Gradient based, Gradient search method, Identification method, Identification problem, Inherent limitations, Levenberg-Marquardt, Local minimums, Local optima, Local search, Memetic, Memetic algorithms, Multi layer perceptron, Multi-input multi-output system, Multi-modal, Nonlinear system identification, One degree of freedom (1-DOF), System identifications, Test rigs, Trained neural networks, Twin-rotors, Backpropagation algorithms, Biology, Cost functions, Furnaces, Genetic algorithms, Mathematical operators, Neural networks, Nonlinear systems, Particle swarm optimization (PSO), article, artificial neural network, back propagation, back propagation algorithm, controlled study, correlation analysis, differential evolution back propagation, evolutionary algorithm, genetic algorithm, machine learning, mathematical computing, memetic differential evolution algorithm, memetic differential evolution trained neural network, nonlinear system, nonlinear system identification, Particle Swarm Optimization, priority journal
Citation
Neurocomputing, 2011, 74, 10, pp. 1696-1709