The Study For Fuzzy Reasoning Algorithms And Pattern Recognition Based On Interval-valued Fuzzy Sets

Fuzzy measures and fuzzy connectives are important tools in the study of fuzzy reasoning algorithms.As generalization of fuzzy sets,interval-valued fuzzy sets and interval-valued intuitionistic fuzzy sets have more advantages in dealing with fuzzy and uncertain information.In this paper,a new fuzzy reasoning algorithm based on a class of interval-valued t-norms with parameters and a new interval-valued intuitionistic fuzzy similarity measure based on transformed triangle fuzzy number are given.Both of them are applied to deal with pattern recognition problems.The specific research contents and the innovations are as follows:(1)A cluster of parametric interval-valued full implication algorithms based on a class of interval-valued t-norms with parameters,and solutions of the proposed algorithm are given.Furthermore,apply the proposed the algorithms to deal with the pattern recognition problems.(2)A cluster of parametric interval-valued quintuple implication algorithms based on a class of interval-valued t-norms with parameters are proposed.The method for pattern recognition is given and applied to solving practical problems.(3)A new similarity measure for interval-valued intuitionistic fuzzy sets is proposed by dividing the interval-valued intuitionistic fuzzy numbers into two intuitionistic fuzzy numbers,calculating the average of the p powers of the points in the triangles composed of two intuitionistic fuzzy numbers.Furthermore,it is applied to the pattern recognition problem.By numerical calculation,compared with the similarity of four interval value intuitionistic fuzzy sets,it is proved that the similarity has higher recognition rate.