Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network

Tafti, Bibi Elham Fallah; Khanesar, Mojtaba Ahmadieh; Teshnehlab, Mohammad

Abstract

In this paper, the integration of Type-2 fuzzy set theory and recurrent wavelet neural network(WNN) is proposed to allow managing of non-uniform uncertainties for identifying non-linear dynamic system. The proposed Type-2 fuzzy WNN is inherently a recurrent multilayered network which constructed based on a set of Type-2 fuzzy rules and recurrent connections in the second layer of the FWNN. Each rule comprises a wavelet function in the consequent part. The structure has both advantages of recurrent and wavelet neural network which expand the basic ability of fuzzy neural network to deal with temporal problems. Both antecedent and consequent parameters update rules are derived based on the gradient descent method. The structure is applied in the identification of dynamic plants which is commonly used in the literature. Simulation result from the identification of a second-order non-linear plant confirms the better performance and effectiveness of the proposed structure.

Citation

Tafti, B. E. F., Khanesar, M. A., & Teshnehlab, M. (2019, January). Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network. Presented at 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Bojnord, Iran

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)
• Start Date: Jan 29, 2019
• End Date: Jan 31, 2019
• Acceptance Date: Dec 22, 2018
• Online Publication Date: Apr 18, 2019
• Publication Date: 2019-01
• Deposit Date: May 10, 2019
• Publicly Available Date: May 10, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-4
• Book Title: Proceedings of 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)
• ISBN: 978-1-7281-0674-8
• DOI: https://doi.org/10.1109/CFIS.2019.8692153
• Keywords: Identification , Recurrent neural network , Wavelet Neural Network , Type-2 fuzzy logic ,
• Public URL: https://nottingham-repository.worktribe.com/output/2037683
• Publisher URL: https://ieeexplore.ieee.org/document/8692153
• Additional Information: © 2019 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.
• Contract Date: May 10, 2019