Conference Paper

A two-step method for nonlinear polynomial model identification based on evolutionary optimization

Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan
DOI: 10.1109/NABIC.2009.5393428 Conference: Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
Source: IEEE Xplore

ABSTRACT A two-step identification method for nonlinear polynomial model using Evolutionary Algorithm (EA) is proposed in this paper, and the method has the ability to select a parsimonious structure from a very large pool of model terms. In a nonlinear polynomial model, the number of candidate monomial terms increases drastically as the order of polynomial model increases, and it is impossible to obtain the accurate model structure directly even with state-of-art algorithms. The proposed method firstly carries out a pre-screening process to select a reasonable number of important monomial terms based on the importance index. In the next step, EA is applied to determine a set of significant terms to be included in the polynomial model. In this way, the whole identification algorithm is implemented very efficiently. Numerical simulations are carried out to demonstrate the effectiveness of the proposed identification method.

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    ABSTRACT: Polynomial NARX (nonlinear autoregressive with exogenous) model identification has received considerable attention in last three decades. However, in a high-order nonlinear system, it is very difficult to obtain the model structure directly even with state-of-art algorithms, because the number of candidate monomial terms is huge and increases drastically as the model order increases. Motivated by this fact, in this research, the identification is performed in two steps: firstly a prescreening process is carried out to select a reasonable number of important monomial terms based on two kinds of the importance indices. Then, in the reduced searching space with only the selected important terms, multi-objective evolutionary algorithm (MOEA) is applied to determine a set of significant terms to be included in the polynomial model with the help of independent validation data. In this way, the whole identification algorithm is implemented efficiently. Numerical simulations are carried out to demonstrate the effectiveness of the proposed identification method. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
    IEEJ Transactions on Electrical and Electronic Engineering 04/2011; 6(3):253 - 259. · 0.34 Impact Factor

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