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论文类型：期刊论文

发表时间：2008-01-01

发表刊物：Zidonghua Xuebao/Acta Automatica Sinica

收录刊物：EI、PKU、ISTIC

卷号：34

期号：1

页面范围：80-84

ISSN号：02544156

摘要：Designing a fuzzy inference system (FIS) from data can be divided into two main phases: Structure identification and parameter optimization. First, starting from a simple initial topology, the membership functions and system rules are defined as specific structures. Second, to speed up the convergence of the learning algorithm and lighten the oscillation, an improved descent method for FIS generation is developed. Furthermore, the convergence and the oscillation of the algorithm are systematically analyzed. Third, using the information obtained from the previous phase, it can be decided in which region of the input space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased. Consequently, this produces a new and more appropriate structure. Finally, the proposed method is applied to the problem of nonlinear function approximation.