INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES

Document Type : Research Paper

Authors

1 DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300-716, KOREA

2 DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST, DAEJEON, 305-701, KOREA

Abstract

The proposed IAFC neural networks have both stability and plasticity because they
use a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.
The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzy
leaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzy
membership values. The supervised IAFC neural networks are the supervised neural networks
which use the fuzzified versions of Learning Vector Quantization (LVQ). In this paper,
several important adaptive learning algorithms are compared from the viewpoint of structure and
learning rule. The performances of several adaptive learning algorithms are compared using
Iris data set.

Keywords

References

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Iranian Journal of Fuzzy Systems
Volume 2, Issue 2 - Serial Number 2
September and October 2005
Pages 1-13

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