Title: Special two-stage input signal based neuro-fuzzy model for Hammerstein-Wiener processes

Authors: Li Jia; Aihua Yang; Minsen Chiu

Addresses: Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China ' Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China ' Department of Chemical and Biomolecular Engineering, National University of Singapore, 117576, Singapore

Abstract: In this paper, a special two-stage input signal based neuro-fuzzy model for Hammerstein-Wiener processes is presented. The input and output non-linear static parts of the Hammerstein-Wiener process are described by two independent neuro-fuzzy models without any prior process knowledge, thus avoiding the inevitable restrictions on static non-linear function encountered by using the polynomial approach. To construct the neuro-fuzzy-based Hammerstein-Wiener model, special two-stage input signal is carried out, and an analytical solution is developed to calculate the parameters of the linear dynamic part and two static non-linear functions. Examples are used to illustrate the applicability of the proposed method and a comparison with polynomial approach is made.

Keywords: Hammerstein-Wiener process; neuro-fuzzy-based modelling; chemical processes; two-stage input signals; neural networks; fuzzy logic.

DOI: 10.1504/IJSCIP.2012.052193

International Journal of System Control and Information Processing, 2012 Vol.1 No.2, pp.199 - 218

Published online: 19 Feb 2013 *

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