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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.

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|June 16, 2023
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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).

Keywords:
Alpha planesFuzzy neural networksGeneral type-2 fuzzy setsLyapunov functionOnline learningSystem identification

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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.