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An analysis of using triangular truth function in fuzzy reasoning based on a fuzzy truth value

Kudłacik P.

Journal of Medical Informatics & Technologies

2013

Vol. 22

103--110

Fuzzy systems are widely used in research and applications considering complex information like gene recognition and classification. Because of the character of genetic data, the extensive knowledge bases of such systems contain complex rules described by even thousands of atomic premises. This paper presents an analysis of fuzzy reasoning based on a fuzzy truth value, presented by Baldwin. The solution is an interesting, alternative approach to fuzzy inference. Considering the Zadeh’s compositional rule of inference, the idea of Baldwin has an advantage of resolving the whole inference process within the truth space. The approach is especially convenient for systems with large number of premises in rules, like mentioned gene classification systems. Although the solution of Baldwin is characterized by significantly lower computational complexity than the full implementation of the compositional rule of inference, it is not applied in contemporary systems. Over the years different researchers proposed simplified approaches, which are easier to implement and faster. The analysis presented in this paper considers possible simplifications that could be applied to the approach of Baldwin, where facts and fuzzy truth values are described by triangular membership functions. Such assumptions open the possibility of implementation of fast Baldwin’s inference and applications even for complex genetic data. Nevertheless, the solution would preserve one of the biggest advantage, which is the fuzzy relation, in form of the truth function, between a fact and a premise, throughout the whole inference process. Other fast approaches reduce this relation to only one value.