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Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

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Title: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks
Author: Patra, Jagdish Chandra; Kot, Alex C.
Copyright year: 2002
Abstract: A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.
Subject: DRNTU::Engineering::Electrical and electronic engineering::Power electronics.
Type: Journal Article
Series/ Journal Title: IEEE transactions on systems, man, and cybernetics, Part B: cybernetics
School: School of Computer Engineering
Rights: © 2002 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI: http://dx.doi.org/10.1109/TSMCB.2002.1018769].
Version: Accepted version

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