| The division, representation and reasoning of knowledge is the basic and essential foundation of the knowledge processing. Fuzzy knowledge is the primary forms in many information processing areas, for instance, experience knowlege in Expert System, the unclear semantic knowledge in Natural Language Understanding (NLU), the obscure semantic words in information retrieval in Web Knowledge Base, environment identification in Automatic Drive and Perceived Environment. For fuzzy knowledge and its different negations, how to deal with the fuzzy knowledge efficiently, including knowledge division, compute, representation and reasoning is the highlight and focus in the information processing.The first chapter in this thesis introduces the development of study about negative information in the world. Chapter 2 mainly advocates the five kinds of negative relations in knowledge concepts, and discussed the essence of concept in negative knowledge. The chapter 3 discussed the capacity of the fuzzy sets FSCOM in fuzzy information processing, as well as the relation to other fuzzy sets.In chapter 4, the paper primary and principal studied an application in a financial investment decision-making case. For different negations in fuzzy knowledge, fuzzy sets～~+A and～–A are elicited, the fuzzy sets' member functions are defined according to the distance-ratio function thought. Further, one confirmed method is presented to define the value ofλas well as the thresholdτin fuzzy production rules. Then the reasoning algorithm and its realization of knowledge are discussed in the case.The basic focus in chapter 5 primary discussed the fuzzy measure in fuzzy sets FSCOM, including similarity measure, distance measure, degree of correlation etc. based on classical fuzzy sets and intuitionistic fuzzy sets theory, besides, the paper suggested a new correctional fuzzy sets FSCOM model, and application to actual fuzzy decision-making analysis, extended the theoretics and reasoning algorithm in fuzzy sets FSCOM.
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