WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification. (20th February 2018)

• Record Type: Journal Article
• Authors: Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.
• Other Names: Andújar José Manuel Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-02-20
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/1753262
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.