Fuzzy controller theory: limit theorems for linear fuzzy control rules
Automatica (Journal of IFAC)
Fuzzy control rules and their natural laws
Fuzzy Sets and Systems
A self-tuning fuzzy controller
Fuzzy Sets and Systems
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
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
Nested design of fuzzy controllers with partial fuzzy rule base
Fuzzy Sets and Systems
Fuzzy Modeling for Control
Fuzzy Logic Foundations and Industrial Applications
Fuzzy Logic Foundations and Industrial Applications
Reduction of fuzzy control rules by means of premise learning - method and case study
Fuzzy Sets and Systems - Fuzzy systems
Fuzzy Logic for Embedded Systems Applications
Fuzzy Logic for Embedded Systems Applications
Fuzzy modelling and tracking control of nonlinear systems
Mathematical and Computer Modelling: An International Journal
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
A FLC approach to torque ripple minimization using direct torque control for asynchronous motors
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A GA optimization for FLC with its rule base and scaling factors adjustment
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule base be altered by combined tuning of membership functions and controller scaling factors. The alternative approach proposed here entails alteration of the fuzzy rule base. The recursive rule base adjustment algorithm proposed in this paper has the benefit that it is computationally more efficient for the generation of a DT, and advantage for online realization. Simulation results are presented to support this thesis.