A learning method of fuzzy inference rules by descent method

The authors propose a learning method for fuzzy inference rules by a descent method. From input-output data gathered from specialists, the inference rules expressing the input-output relation of the data are obtained automatically. The membership functions in the antecedent part and the real number in the consequent part of the inference rules are tuned by means of the descent method. The learning speed and the generalization capability of this method are higher than those of a conventional backpropagation type neural network. This method has the capability to express the knowledge acquired from input-output data in the form of fuzzy inference rules. Some numerical examples are described to show these advantages over the conventional neural network. An application of the method to a mobile robot that avoids a moving obstacle and its computer simulation are reported