00000cab a22000003a 4500 UP-99796217609532826 Buklod 20231007234346.0 m |o d | ta 101109s xx d | ||r ||||| || DENGII eng Ji-Cheng Duan Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning. pp. 293-306 In recent years, there has been an increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fuzzy neural network (FNN) models have been proposed to implement different types of single-stage fuzzy reasoning mechanisms. Single-stage fuzzy reasoning, however, is only the most basic among a human being's various types of reasoning mechanisms. Syllogistic fuzzy reasoning, where the consequence of a rule in one reasoning stage is passed to the next stage as a fact, is essential to effectively build up a large scale system with high level intelligence. In view of the fact that the fusion of syllogistic fuzzy logic and neural networks has not been sufficiently studied, a new FNN model based on syllogistic fuzzy reasoning, termed cascaded FNN (CFNN), is proposed in this paper. From the stipulated input-output data pairs, the model can generate an appropriate syllogistic fuzzy rule set through structure (genetic) learning and parameter (back-propagation) learning procedures proposed in this paper. In addition, we particularly discuss and analyze the performance of the proposed model in terms of approximation ability and robustness as compared with single-stage FNN models. The effectiveness of the proposed CFNN model is demonstrated through simulating two benchmark problems in fuzzy control and nonlinear function approximation domain CFNN. I/O data pairs. Back-propagation learning. Backpropagation learning. Cascaded FNN. Cascaded fuzzy neural network model. Fuzzy control. Fuzzy logic. Input-output data pairs. Learning. Nonlinear function approximation. Parameter learning. Single-stage fuzzy reasoning mechanisms. Structure learning. Syllogistic fuzzy reasoning. IEEE Transactions on fuzzy systems 9, 2 (2001). FO UPD DENG-II Article