期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2009
卷号：5
DOI：10.1080/15501320802524037
出版社：Hindawi Publishing Corporation
摘要：In this paper, a novel Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) is constructed for modeling and sensor fault diagnosis of a Continuous Stirred Tank Reactor (CSTR), a nonlinear dynamic system. The TSRFNN is composed of 9 layers, including premise network and consequence network. The temporal information is embedded in the TSRFNN by adding the feedback connections between the output layer and the input layer of the fuzzy neural network (FNN). It is assumed that the inputs are Gaussian membership functions; the product operation is utilized for the premise and implication, and the weighted center-average method is adopted for defuzzification. The network is a Fuzzy Basis Function(FBF). The general approximation characteristic of the network was proven by the theory reasoning. The identification of the TSRFNN consists of two steps: structure identification and parameter identification. Unsupervised clustering is used to determine the structure of the fuzzy system, the number of fuzzy rules, and the membership functions of the premise using the input-output data of a system. Then in the parameter identification, the Dynamic Backpropagation (DBP) is adopted to determine the membership functions of the conclusion of the fuzzy system.
