Parametric tuning of rule-based systems by maximum fuzzy entropy

Fuzzy production rules (FPRs) are widely used in expert systems to represent uncertainty concepts. In order to enhance the representation capability and to improve the reasoning-accuracy of FPRs, some useful knowledge representation parameters such as certainty factor, local weight and global weight have been included in FPRs. However, the acquisition of the values of these parameters is difficult and time-consuming. Usually the principle to determine these parameters is to further reduce the training error. This paper proposes a new principle, i.e., the maximum entropy principle, for solving these parameters. Firstly we present a parametric tuning method based on the maximization of fuzzy entropy on the training set, then a genetic algorithm-based optimization technique is applied to determine the values of the weights in FPRs. Experimental results demonstrate a number of advantages of our method such as automatic acquisition of the weights, avoiding the over-fitting to a great extent and non-changing the number of the initial FPRs.