A New Algorithm of Closeness Degree for Fuzzy Pattern Recognition

There are two basic methods of fuzzy pattern recognition: one is direct method based on membership degree, and another is indirect method based on closeness degree. In this paper, different algorithms of closeness degree have been studied and a new method for calculating the closeness degree has been introduced. By combining the concepts of membership degree and closeness degree, the pattern characteristic values derived from both real numbers and fuzzy set expressions can be handled, then soft transition from direct method to indirect method can be realized by the new algorithm; It also could adjust every kind of weights of closeness degree to adapt itself based on the characteristics of classical model and be recognized model. And some case studies would be provided to demonstrate the effectiveness and availability for the new algorithm. The algorithm could also be used in different circumstances to deal with closeness degree other than pattern recognition.

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