Interval type-3 fuzzy Wiener model for nonlinear dynamic systems: Application to continuous stirred tank reactor

Tarek R. Khalifa, Xian Yu, Amin Sharafian, Xiaopin Zhong and Zongze Wu

Chaos, Solitons & Fractals, 2025, vol. 199, issue P1

Abstract: This paper proposes a novel interval type-3 Takagi–Sugeno–Kang fuzzy Wiener model (IT3TSK-FWM) to address the challenge of modeling nonlinear dynamic systems with complex uncertainties. The proposed model adopts a Wiener structure, where the linear dynamic part is represented by an autoregressive moving average (ARMA) system and the nonlinear static part is represented by an IT3TSK fuzzy system. First, we introduce a novel rule firing strength-based algorithm to eliminate the dependency on predefined structures, allowing the proposed IT3TSK-FWM to adapt its structure online. Then, we introduce a novel Lyapunov-based learning algorithm to ensure convergence of the estimation error to zero. Furthermore, we employ a direct defuzzification method to enhance computational efficiency. Finally, the effectiveness of the IT3TSK-FWM is demonstrated through its application to a continuous stirred tank reactor system, validating the model’s robustness and performance in handling nonlinear dynamics.

Keywords: Interval type-3 fuzzy systems; Continuous stirred tank reactor (CSTR); Structure learning; Parameter learning; Nonlinear dynamic systems (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:199:y:2025:i:p1:s0960077925005971

DOI: 10.1016/j.chaos.2025.116584

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