SUMMARYWe already proposed the freehand curve identifier FSCI, based on fuzzy theory, as a freehand figure recognition engine. We next investigated the improvement of the recognition rate of FSCI by learning optimization of parameters. This paper proposes improvements of the FSCI algorithm itself, namely, improvement of the hypothetical fuzzy model formulation, and introduction of necessity into the fuzzy inference rules. It was shown by experiments that these improvements have effects which cannot be obtained by conventional parameter adjustment.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.