Structure identification of fuzzy model
Fuzzy Sets and Systems
An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
An input-output clustering approach to the synthesis of ANFIS networks
IEEE Transactions on Fuzzy Systems
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
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This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.