Linguistic recognition system based on approximate reasoning

A linguistic recognition system based on approximate reasoning has been described which is capable of handling various imprecise input patterns and of providing a natural decision. The input feature is considered to be of either linguistic form or quantitative form or mixed form or set form. An input has been viewed as consisting of various combinations of the three primary properties small, medium and high possessed by its different features to some degree. The various uncertainty (ambiguity) in the input statement has been managed by providing/modifying membership values heuristically to a great extent. Unlike the conventional fuzzy set theoretic approach, the sets small and high have been represented here by π-functions. The weight matrices corresponding to various properties and classes have been taken into account in the composition rule of inference in order to make the analysis more effective. The natural output decision is associated with a confidence factor denoting the degree of certainty of the decision, thus providing a low rate of misclassification as compared to the conventional two-state system. The effectiveness of the algorithm has been demonstrated on the speech recognition problem