Performance improvement of fuzzy RBF networks

Authors:
Kwang-Baek Kim;Dong-Un Lee;Kwee-Bo Sim
Affiliations:
Dept. of Computer Engineering, Silla University, Korea;School of Architectural Engineering, Pusan National University, Korea;School of Electrical and Electronic Engineering, Chung-Ang Univ., Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

In this paper, we propose an improved fuzzy RBF network which dynamically adjusts the rate of learning by applying the Delta-bar-Delta algorithm in order to improve the learning performance of fuzzy RBF networks. The proposed learning algorithm, which combines the fuzzy C-Means algorithm with the generalized delta learning method, improves its learning performance by dynamically adjusting the rate of learning. The adjustment of learning rate is achieved by self-generating middle-layered nodes and applying the Delta-bar-Delta algorithm to the generalized delta learning method for the learning of middle and output layers. To evaluate the learning performance of the proposed RBF network, we used 40 identifiers extracted from a container image as the training data. Our experimental results show that the proposed method consumes less training time and improves the convergence of learning, compared to the conventional ART2-based RBF network and fuzzy RBF network.