A Statistical fuzzy inference system for classifying human constituents

Abstract

In this paper, statistical fuzzy inference system based on principal component analysis (PCA) and Fuzzy Expert system for diagnosis of human constituents is introduced. This statistical fuzzy inference system deals with combination of the filtering and lassification from measured PCA and Fuzzy expert system technology. This intelligent system has three phases. In acquiring tacit knowledge phase, the model refinement and reasoning for diagnosis of human constituents performed. Tacit knowledge in Ayurvedic subdomain of individual classification has been acquired through a questionnaire and analyzed to identify the dependencies, which lead to make tacit knowledge in the particular domain. In the first place analysis was done using statistical techniques of principal components and the results were not compatible with the experiences of Ayurvedic experts. The result of the modeling of Ayurvedic domain using fuzzy logic has been compatible with the experiences of the Ayurvedic experts. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. The development has been done using Visual basic, FLEX expert system shell and the system runs on Windows platform.