A fast learning algorithm for parismonious fuzzy neural systems

Authors:
Meng Joo Er;Shiqian Wu
Affiliations:
Intelligent Machine Research Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore 639798, Singapore;Center for Signal Processing, Innovation Centre, Block 1, 16, Nanyang Drive, Singapore 637722, Singapore
Venue:
Fuzzy Sets and Systems - Information processing
Year:
2002

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Abstract

In this paper, a novel learning algorithm for dynamic fuzzy neural networks based on extended radial basis function neural networks, which are functionally equivalent to Takagi-Sugeno-Kang fuzzy systems, is proposed. The algorithm comprises 4 parts: (1) criteria of rules generation; (2) allocation of premise parameters; (3) determination of consequent parameters and (4) pruning technology. The salient characteristics of the approach are: (1) a hierarchical on-line self-organizing learning paradigm is employed so that not only parameters can be adjusted, but also the determination of structure can be self-adaptive without partitioning the input space a priori; (2) fast learning speed can be achieved so that the system can be implemented in real time. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that the proposed algorithm is superior in terms of simplicity of structure, learning efficiency and performance.