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论文类型：期刊论文

发表时间：2010-05-01

发表刊物：INFORMATION SCIENCES

收录刊物：SCIE、EI、Scopus

卷号：180

期号：9

页面范围：1630-1642

ISSN号：0020-0255

关键字：Zero-order Takagi-Sugeno inference system; Modified gradient-based neuro-fuzzy learning algorithm; Convergence; Constant learning rate; Gaussian membership function

摘要：Neuro-fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems. In this paper, a modified gradient-based neuro-fuzzy learning algorithm is proposed for zero-order Takagi-Sugeno inference systems. This modified algorithm, compared with conventional gradient-based neuro-fuzzy learning algorithm, reduces the cost of calculating the gradient of the error function and improves the learning efficiency. Some weak and strong convergence results for this algorithm are proved, indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value, respectively. A constant learning rate is used. Some conditions for the constant learning rate to guarantee the convergence are specified. Numerical examples are provided to support the theoretical findings. (C) 2010 Elsevier Inc. All rights reserved.