Abstract
ABSTRACTIn this paper, a new adaptive fuzzy reasoning technique using compensatory DNF-CNF operations is proposed according to the compensatory principle and interval valued fuzzy sets. The compensatory FDNF-FCNF neurofuzzy system using the control-oriented fuzzy neurons and the decision-oriented fuzzy neurons can not only adjust fuzzy membership functions but also optimize the adaptive fuzzy reasoning by using the compensatory learning algorithm. This system can effectively be used to learn the fuzzy rules of two players in a game from given data, then transforms a local game to a global game, and finally makes better fuzzy moves based on the global game. In addition, simulations have indicated that the convergence speed of the compensatory FDNF-FCNF learning algorithm is faster than that of the conventional backpropagation algorithm.
Published Version
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