A self-tuning fuzzy controller
Fuzzy Sets and Systems
PID type fuzzy controller and parameters adaptive method
Fuzzy Sets and Systems
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
A course in fuzzy systems and control
A course in fuzzy systems and control
A PD-like self-tuning fuzzy controller without steady-state error
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Tuning fuzzy logic controllers using response envelope method
Fuzzy Sets and Systems
Auto-tuning of fuzzy logic controllers for self-regulating processes
Fuzzy Sets and Systems
Reduction of fuzzy control rules by means of premise learning - method and case study
Fuzzy Sets and Systems - Fuzzy systems
A recursive rule base adjustment algorithm for a fuzzy logic controller
Fuzzy Sets and Systems
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This paper introduces a Genetic Algorithm (GA) based optimization for rule base and scaling factors adjustment to enhance the performance of fuzzy logic controllers. First a recursive rule base adjustment algorithm is developed, which has the benefit that it is computationally more efficient for the generation of a decision table. Then utilizing the advantage of GA optimization, a novel approach that each random combination of the optimized parameters (including the membership function selection for the rule base and controller scaling factors) is coded into a Real Coded string and treated as a chromosome in genetic algorithms is given. The optimization for rule base with the correspondent membership function and scaling factors using GA is easy to be realization in engineering. Simulation results are presented to support this thesis.