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A recurrent self-organizing neural fuzzy inference network.

C F Juang¹, C T Lin

¹Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

A novel recurrent self-organizing neural fuzzy inference network (RSONFIN) is introduced for dynamic temporal problems. This network automatically learns fuzzy rules and parameters, eliminating the need for predetermination and offering efficient solutions.

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Area of Science:

Artificial Intelligence
Computational Intelligence
Machine Learning

Background:

Dynamic fuzzy inference systems require effective modeling of temporal relations.
Existing neural fuzzy networks often lack inherent mechanisms for temporal data processing.
Predetermining network structure, such as the number of hidden nodes, can be a significant limitation.

Purpose of the Study:

To propose a novel recurrent self-organizing neural fuzzy inference network (RSONFIN).
To enable on-line concurrent structure and parameter identification for dynamic fuzzy rules.
To develop a network suitable for temporal problems without predefining its structure.

Main Methods:

The RSONFIN integrates feedback connections into a feedforward neural fuzzy network to capture temporal dependencies.
Concurrent structure identification (fuzzy rule construction) and parameter identification (membership function tuning) are employed.
An aligned clustering-based algorithm is utilized for flexible input partitioning to reduce fuzzy rules.

Main Results:

The RSONFIN demonstrates inherent suitability for temporal problems due to its recurrent architecture.
The network automatically and rapidly determines its optimal structure and parameters, avoiding predetermination.
Simulations on temporal problems show efficient performance and favorable comparisons with existing recurrent networks.

Conclusions:

The RSONFIN provides an effective framework for dynamic fuzzy inference in temporal domains.
The proposed on-line learning mechanism allows for automatic and efficient network construction.
The RSONFIN offers a powerful and adaptable solution for complex temporal data processing challenges.