In this paper, we propose a fuzzy reinforcement algorithm, which map continuous state Spaces to continuous action Spaces by fuzzyinference system and then learn a rule base.
A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzyinference system and then learn a rule base.
A novel hybrid neural fuzzyinference system is presented. Only based on the desired input output data pairs, are the knowledge acquisition and initial fuzzyrule sets available.