Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Fuzzy-neural control: principles, algorithms and applications
Fuzzy-neural control: principles, algorithms and applications
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Genetic algorithms for learning the rule base of fuzzy logic controller
Fuzzy Sets and Systems
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
A self-generating method for fuzzy system design
Fuzzy Sets and Systems
A new approach to the identification of a fuzzy model
Fuzzy Sets and Systems
A fuzzy backpropagation algorithm
Fuzzy Sets and Systems
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Fuzzy control of multivariable nonlinear servomechanisms with explicit decoupling scheme
IEEE Transactions on Fuzzy Systems
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Hysteresis modeling of piezoelectric actuators using the fuzzy system
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
A new method for semi-automatic fuzzy training and its application in environmental modeling
Environmental Modelling & Software
Advances in Engineering Software
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This paper proposes a simple algorithm for training fuzzy systems from numerical data. The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method.