In this paper, a method of multipoint location is raised based on the passive detection system and the mathematic model and error model is set up used to measure position. Analyzing a detection system relationship of minimal location fuzziness area and range, relationship of location space error, bearing angle and relationship of circular probable error of multipoint location and range and giving the simulation curve. From the result of the simulation, the location precision of this passive detection system is high. But which should be paid attention to is that the aero error and some other system error are not in the error model, in fact, those errors have a great effect on the location precision of this passive detection system. By corrections, the system error can be reduced, but which can't be removed completely.
Phillips & Silverstein's ambitious link between receptor abnormalities and the symptoms of schizophrenia involves a certain amount of fuzziness; No detailed mechanism is suggested through which the proposed abnormality would lead to psychological traits. We propose that detailed simulation of brain regions, using model neural networks, can aid in understanding the relation between biological abnormality and psychological dysfunction in schizophrenia.
Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.
While studies of the role of fuzzy logic in natural language certainly exist, it is not clear that the use of fuzzy logic to represent linguistic constructs is anything more than an engineering convenience. This paper suggests that one reason this situation obtains is because fuzzy logic has been used strictly to elucidate static aspects of natural language (particularly aspects of the lexicon). If one examines dynamic features of natural language, on the other hand, new possibilities for connections between fuzzy logic and natural language emerge. In particular, some results from category theory are used to show that fuzzy logic can have a role in explaining certain otherwise rather obscure properties of linguistic comparatives in English.
Each individual investor is different, with different financial goals, levels of risk tolerance and personal preferences. From the point of view of investment management, these characteristics are often defined as objectives and constraints. Objectives can be the type of return being sought, while constraints include factors such as time horizon, how liquid the investor is, any personal tax situation and how risk is handled. It is really a balancing act between risk and return with each investor having unique requirements, as well as a unique financial outlook - essentially a constrained utility maximization objective. To analyze how well a customer fits into a particular investor class, one investment house has even designed a structured questionnaire with about 24 questions that each has to be answered with values from 1 to 5. The questions range from personal background to what the customer expects from an investment. A fuzzy logic system has been designed for the evaluation of the answers to the above questions. The notion of fuzziness with respect to funds allocation is investigated.