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.

[1]  Henri Prade,et al.  On the Problems of Representation and Propagation of Uncertainty in Expert Systems , 1985, Int. J. Man Mach. Stud..

[2]  Madan M. Gupta,et al.  Approximate reasoning in expert systems , 1985 .

[3]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[4]  S. Pal.  Optimum guard zone for self-supervised learning , 1982 .

[5]  Sankar K. Pal,et al.  Fuzzy Mathematical Approach to Pattern Recognition , 1986 .

[6]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[7]  S. Ray,et al.  Maximum Likelihood Methods in Vowel Recognition: A Comparative Study , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  T. Pavlidis,et al.  Fuzzy sets and their applications to cognitive and decision processes , 1977 .

[9]  Amita Pathak,et al.  Dynamic guard zone for self-supervised learning , 1988, Pattern Recognit. Lett..

[10]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[11]  Tsu-Tian Lee,et al.  On the design of a classifier with linguistic variables as inputs , 1983 .

[12]  Ronald R. Yager,et al.  Multiple objective decision-making using fuzzy sets , 1977 .

[13]  A. Kandel.  Fuzzy Mathematical Techniques With Applications , 1986 .

[14]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[15]  R. Yager.  VALIDATION OF FUZZY-LINGUISTIC MODELS , 1978 .

[16]  J. Baldwin.  A new approach to approximate reasoning using a fuzzy logic , 1979 .

[17]  Sankar K. Pal,et al.  Fuzzy sets and decisionmaking approaches in vowel and speaker recognition , 1977 .

[18]  David G. Stork,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .