An improved learning scheme for extracting t-s fuzzy rules from data samples

Authors:
Ning Wang;Xuming Wang;Yue Tan;Pingbo Shao;Min Han
Affiliations:
Marine Engineering College, Dalian Maritime University, Dalian, China,Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

In this paper, we present an improved learning scheme for extracting T-S fuzzy rules from data samples, whereby a neuro-fuzzy architecture implements the T-S fuzzy system using ellipsoidal basis functions. The salient characteristics of this approach are as follows: 1) A novel structure learning algorithm incorporating a pruning strategy into new growth criteria is developed. 2) Compact fuzzy rules can be extracted from training data. 3) The linear least squares (LLS) method is employed to update consequent parameters, and thereby contributing to high approximation accuracy. Simulation studies and comprehensive comparisons with other well-known algorithms demonstrate the effective and superior performance of our proposed scheme in terms of compact structure and promising accuracy.