Recurrent general type-2 fuzzy neural networks for nonlinear dynamic systems identification
Ahmad M El-Nagar1, Mohammad El-Bardini1, A Aziz Khater1
1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menof, 32852, Egypt.
ISA Transactions
|June 16, 2023
Summary
This study presents a novel recurrent general type-2 Takagi-Sugeno-Kang fuzzy neural network (RGT2-TSKFNN) for nonlinear system identification. The RGT2-TSKFNN effectively handles data uncertainties using general type-2 fuzzy sets (GT2FS) and recurrent fuzzy neural networks (RFNN).
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Computational Intelligence
Background:
- Nonlinear systems present significant challenges in accurate identification and modeling.
- Data uncertainties in system dynamics necessitate robust identification techniques.
- Existing type-2 fuzzy neural networks (T2FNNs) may face computational limitations and stability concerns.
Purpose of the Study:
- To introduce a novel recurrent general type-2 Takagi-Sugeno-Kang fuzzy neural network (RGT2-TSKFNN) for enhanced nonlinear system identification.
- To address data uncertainties by integrating general type-2 fuzzy sets (GT2FS) with recurrent fuzzy neural networks (RFNN).
- To develop an efficient strategy for constructing and training the RGT2-TSKFNN, ensuring stability and reducing computational load.
Main Methods:
- The proposed RGT2-TSKFNN combines GT2FS for antecedents and TSK type for consequents, with fuzzy firing strengths as internal variables.
- A type-reduction strategy using alpha-cuts decomposes GT2FS into interval type-2 fuzzy sets (IT2FSs).
- Direct defuzzification, type-2 fuzzy clustering, and Lyapunov criteria are employed for efficient parameter and structure learning, ensuring stability.
Main Results:
- The developed RGT2-TSKFNN effectively identifies nonlinear systems while managing data uncertainties.
- An efficient type-reduction method using alpha-cuts and direct defuzzification significantly reduces computation time compared to iterative methods like Karnik-Mendel (KM).
- Online structure and parameter learning using fuzzy clustering and Lyapunov criteria ensure stability and rule reduction.
Conclusions:
- The RGT2-TSKFNN offers a robust and computationally efficient solution for nonlinear system identification in the presence of uncertainties.
- The proposed methods for type reduction and learning contribute to the stability and performance of type-2 fuzzy neural networks.
- Comparative analyses demonstrate the superior performance of the RGT2-TSKFNN over existing T2FNN methodologies.
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