Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
GA-based fuzzy reinforcement learning for control of a magneticbearing system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
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RoboCup 2001: Robot Soccer World Cup V
Dynamically balanced optimal gaits of a ditch-crossing biped robot
Robotics and Autonomous Systems
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This paper presents an evolutionary approach to learning a fuzzy logic controller (FLC) employed for reactive behaviour control of Sony legged robots. The learning scheme is divided into two stages. The first stage is a structure learning in which the rule base of FLC is generated by a backup updating learning. The second stage is a parameter learning in which the parameters of membership functions of fuzzy sets are learned by a genetic algorithm (GA). Simulation results are provided to show the effectiveness of the proposed learning scheme.