GA-based Fuzzy System Design in FPGA for an Omni-directional Mobile Robot
Journal of Intelligent and Robotic Systems
Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling
Fuzzy Sets and Systems
Hybrid learning-based neuro-fuzzy inference system: a new approach for system modeling
International Journal of Systems Science
Fewer Hyper-Ellipsoids Fuzzy Rules Generation Using Evolutional Learning Scheme
Cybernetics and Systems
Design and Implementation of GA-based Fuzzy System on FPGA CHIP
Cybernetics and Systems
CMAC-based neuro-fuzzy approach for complex system modeling
Neurocomputing
A PSO-Fuzzy group decision-making support system in vehicle performance evaluation
Mathematical and Computer Modelling: An International Journal
Active control of friction self-excited vibration using neuro-fuzzy and data mining techniques
Expert Systems with Applications: An International Journal
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A method based on the concepts of genetic algorithm (GA) and recursive least-squares method is proposed to construct a fuzzy system directly from some gathered input-output data of the discussed problem. The proposed method can find an appropriate fuzzy system with a low number of rules to approach an identified system under the condition that the constructed fuzzy system must satisfy a predetermined acceptable performance. In this method, each individual in the population is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and the recursive least-squares method is used to determine the consequent part of the constructed fuzzy system described by this individual. Finally, three identification problems of nonlinear systems are utilized to illustrate the effectiveness of the proposed method.