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Data-based system modeling using a type-2 fuzzy neural network with a hybrid learning algorithm.

Chi-Yuan Yeh¹, Wen-Hau Roger Jeng, Shie-Jue Lee

¹Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan. yuan@water.ee.nsysu.edu.tw

IEEE Transactions on Neural Networks

|October 20, 2011

Summary

This summary is machine-generated.

This study introduces a new method for creating type-2 neural-fuzzy systems using clustering and a hybrid learning algorithm. The approach effectively builds fuzzy rule bases and refines parameters for accurate system outputs.

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Area of Science:

Computational Intelligence
Machine Learning
Fuzzy Systems

Background:

Type-2 fuzzy systems offer enhanced uncertainty handling compared to type-1 systems.
Developing effective type-2 neural-fuzzy systems from data remains a challenge.

Purpose of the Study:

To propose a novel, self-constructing approach for building type-2 neural-fuzzy systems.
To effectively partition data and derive fuzzy rules for system construction.
To refine system parameters using a hybrid optimization algorithm.

Main Methods:

A self-constructing fuzzy clustering method partitions input-output data based on similarity.
Type-2 fuzzy Takagi-Sugeno-Kang IF-THEN rules are derived from clusters.
A hybrid learning algorithm combining particle swarm optimization and least squares estimation refines parameters.
A refined type reduction algorithm is used for defuzzification.

Main Results:

The proposed method successfully constructs a type-2 neural-fuzzy system from training data.
The hybrid learning algorithm effectively refines system parameters.
Experimental results demonstrate the effectiveness of the developed approach.

Conclusions:

The novel approach provides an effective framework for building type-2 neural-fuzzy systems.
The integration of fuzzy clustering and hybrid learning enhances system performance.
This method offers a robust solution for data-driven fuzzy system development.