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Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control

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

Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control Thumbnail


Authors

Bibi Elham Fallah Tafti

Mohammad Teshnehlab



Abstract

Fuzzy neural networks, with suitable learning strategy, have been demonstrated as an effective tool for online data modeling. However, it is a challenging task to construct a model to ensure its quality and stability for non-stationary dynamic systems with some uncertainties. To solve this problem, this paper presents a novel identification model based on recurrent interval type-2 fuzzy wavelet neural network (RIT2FWNN) with new learning algorithm. The model benefits from both advantages of recurrent and wavelet neural networks such as use of temporal data and fast convergence properties. The proposed antecedent and consequent parameters update rules are derived using sliding-mode-control-theory. To evaluate the proposed fuzzy model, it is utilized to design a nonlinear model-based predictive controller and is applied for the synchronization of fractional-order time-delay chaotic systems. Using Lyapunov stability analysis, it is shown that all update rules of the parameters are uniformly ultimately bounded. The adaptation laws obtained in this method are very simple and have closed forms. Some stability conditions are derived to prove learning dynamics and asymptotic stability of the network by using an appropriate Lyapunov function. The efficacy and performance of the proposed method is verified by simulation examples.

Citation

Tafti, B. E. F., Teshnehlab, M., & Khanesar, M. A. (2020). Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control. International Journal of Fuzzy Systems, 22, 351–367. https://doi.org/10.1007/s40815-019-00766-z

Journal Article Type Article
Acceptance Date Oct 30, 2019
Online Publication Date Feb 13, 2020
Publication Date 2020-03
Deposit Date Feb 24, 2020
Publicly Available Date Mar 29, 2024
Journal International Journal of Fuzzy Systems
Print ISSN 1562-2479
Electronic ISSN 2199-3211
Peer Reviewed Peer Reviewed
Volume 22
Pages 351–367
DOI https://doi.org/10.1007/s40815-019-00766-z
Keywords Theoretical Computer Science; Computational Theory and Mathematics; Software; Artificial Intelligence
Public URL https://nottingham-repository.worktribe.com/output/4026911
Publisher URL https://link.springer.com/article/10.1007/s40815-019-00766-z
Additional Information This is a post-peer-review, pre-copyedit version of an article published in International Journal of Fuzzy Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s40815-019-00766-z

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