Simultaneous training of fuzziness generator and inference rules in the freehand curve identifier FSCI

Abstract

SUMMARYFreehand curve recognition method FSCI is already proposed and consists of the following procedure. The freehand drawing is represented as a fuzzy curve model by using a fuzziness generator, and fuzzy inference is used to find as simple a geometrical curve as possible, so that the drawing intention of the user is identified. It is recognized that the adequacy of the parameter setting in the fuzziness generator and the inference rules has a serious effect on the identification performance of FSCI. Thus, training optimization for the fuzziness generator using the genetic algorithm, and training optimization of inference rules using a neural network have been separately proposed. This paper proposes a new method which achieves simultaneous training optimization of the fuzziness generator and the inference rules using a neural network. The effectiveness of the method is demonstrated experimentally.