Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/81431
Title: Fuzzy identification and controller based on generalized fuzzy radial basis function networks
Authors: Zhang, Xinghu
Hang, Chang-Chieh 
Tan, Shaohua 
Wang, Pei-Zhuang 
Issue Date: 1995
Citation: Zhang, Xinghu,Hang, Chang-Chieh,Tan, Shaohua,Wang, Pei-Zhuang (1995). Fuzzy identification and controller based on generalized fuzzy radial basis function networks. IEEE International Conference on Fuzzy Systems 1 : 239-246. ScholarBank@NUS Repository.
Abstract: This paper first proposes a new kind of fuzzy neural networks - Generalized Fuzzy Radial Basis Function Networks (f-RBF), which combines the fuzzifying and defuzzifying processes into a united network structure. We then give the dynamic training rule and training strategy for the f-RBF. We further discuss several special features of this kind of networks that conventional neural networks do not have, and conclude that it can process both the fuzzy-valued and real-valued data simultaneously, and can achieve the minimum realization of fuzzy controller for nonlinear systems. Finally, using the f-RBF, we design a fuzzy controller for a nonlinear system regulation. Furthermore, we point out that any nonlinear control u can be decomposed into three parts: a fuzzy control uf, a linear control ul, and an error compensation ue, i.e., u = uf + ul + ue. The stability of the closed-loop system is also analyzed using sliding control techniques.
Source Title: IEEE International Conference on Fuzzy Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/81431
Appears in Collections:Staff Publications

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