Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy control and fuzzy systems (2nd, extended ed.)
Fuzzy control and fuzzy systems (2nd, extended ed.)
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A knowledge-base generating hierarchical fuzzy-neural controller
IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
A 2-Opt based differential evolution for global optimization
Applied Soft Computing
Rule base identification in fuzzy networks by Boolean matrix equations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers (HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub-controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution (DE). The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two-stage HFLC for controlling a cart-pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods.