On identification of structures in premises of a fuzzy model using a fuzzy neural network

The fuzzy neural networks (FNNs) proposed are multilayered backpropagation (BP) models where the structures are designed to realize fuzzy reasoning and to make the connection weights of the networks correspond to the parameters of the fuzzy reasoning. By modifying the connection weights of the network through learning with the BP algorithm, the FNNs can identify the fuzzy rules and tune the membership functions of the fuzzy reasoning automatically. The authors study the tuning of the membership functions in the premises of an FNN using the input-output data for which the characteristics are known, and show that the BP algorithm realizes the appropriate tuning for representing the characteristics of teaching signals. Based on the results of this study, a method is presented to identify the fuzzy models with the minimal number of the membership functions in the premises