Special Issue on Neuro-Computing and Hybrid Methods for Evolving IntelligenceNo Access

EVOLUTIONARY COMPUTATION FOR ON-LINE AND OFF-LINE PARAMETER TUNING OF EVOLVING FUZZY NEURAL NETWORKS

ZEKE S. H. CHAN

Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

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and

NIKOLA KASABOV

Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

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https://doi.org/10.1142/S1469026804001331Cited by:14 (Source: Crossref)

Abstract

This work applies Evolutionary Computation to achieve completely self-adapting Evolving Fuzzy Neural Networks (EFuNNs) for operating in both incremental (on-line) and batch (off-line) modes. EFuNNs belong to a class of Evolving Connectionist Systems (ECOS), capable of performing clustering-based, on-line, local area learning and rule extraction. Through Evolutionary Computation, its parameters such as learning rates and membership functions are continuously adjusted to reflect the changes in the dynamics of incoming data. The proposed methods are tested on the Mackey–Glass series and the results demonstrate a substantial improvement in EFuNN's performance.

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