Open AccessSimultaneous training of fuzziness generator and inference rules in the freehand curve identifier FSCI
Author(s) -
Oguma Motoaki,
Takikawa Hiroyasu,
Saga Sato
Publication year - 2007
Publication title -
systems and computers in japan
Language(s) - English
Resource type - Journals
eISSN - 1520-684X
pISSN - 0882-1666
DOI - 10.1002/scj.10387
Subject(s) - computer science , generator (circuit theory) , inference , identifier , adaptive neuro fuzzy inference system , fuzzy inference , artificial intelligence , artificial neural network , machine learning , identification (biology) , fuzzy logic , fuzzy inference system , training (meteorology) , data mining , fuzzy control system , power (physics) , physics , botany , quantum mechanics , meteorology , biology , programming language
Freehand 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. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 38(1): 51– 61, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/scj.10387.