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On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm.

S I Horikawa¹, T Furuhashi, Y Uchikawa

¹Dept. of Electron.-Mech. Eng., Nagoya Univ.

IEEE Transactions on Neural Networks

|January 1, 1992

Summary

This study introduces a fuzzy modeling method using fuzzy neural networks and the backpropagation algorithm for automatic nonlinear system identification. The approach was validated using numerical data, proving its feasibility.

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Area of Science:

Artificial Intelligence
Computational Intelligence
Control Systems Engineering

Background:

Nonlinear systems present significant challenges in accurate modeling and control.
Traditional modeling techniques often struggle with the complexity and dynamic nature of nonlinear systems.
Fuzzy logic and neural networks offer powerful tools for approximating complex system behaviors.

Purpose of the Study:

To present a novel fuzzy modeling method for automatic identification of nonlinear systems.
To integrate fuzzy neural networks with the backpropagation algorithm for enhanced model learning.
To demonstrate the practical applicability of the proposed method through numerical simulations.

Main Methods:

Development of a fuzzy modeling approach leveraging fuzzy neural networks.

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Implementation of the backpropagation algorithm for training and parameter optimization.

Application of the method to identify a nonlinear system using synthetic data.

Main Results:

The fuzzy neural network successfully identified the fuzzy model of the nonlinear system.
Automatic model identification was achieved without manual rule base design.
The method demonstrated feasibility and effectiveness in handling nonlinear system dynamics.

Conclusions:

The proposed fuzzy modeling method offers an effective and automated solution for nonlinear system identification.
Fuzzy neural networks combined with backpropagation provide a robust framework for learning complex system models.
This approach has potential applications in various fields requiring accurate nonlinear system analysis and control.