In this paper, we have provided some definitions and theorems about fuzzy set, set operations, the uncertainty theory used in environments, credibility which exists on base of mathematical knowledge. Then, definition applications fuzzification, defuzzification, similarity, distribution were included diagnostic medicine. Sounds respiratory patients transferred to computer by recording converted ...

In this paper, we purposed a new method in modeling the uncertainty data based on the theory of fuzzy sets and the interpolation B-spline model curve. The uncertainty data firstly defined by using fuzzy number concept and fuzzy relation by defined them in real data form. The existed coefficient control of interpolation B-spline basis function is substituted with the fuzzy data points(FDPs) whic...

The notion of ordering is perhaps one of the most fundamental of abstract concepts. The zeta function is used to algebraically describe the ordering of elements in a lattice. An appropriate generalization of the zeta function generalizes the notion of inclusion to degrees of inclusion. However, the lattice structure imposes strong constraints on the values that these degrees can take. Here we r...

Many studies about learning in limited data were made in recent years. Without double, small data set learning is a challenging problem. Information in data of small size is scarce and has some learning limit. While discussing the learning accuracy in limited data, different classification method causes different results for different data because each classification method has its property. A ...

This paper proposes a rule base compression method for fuzzy systems. The method is based on filtration of rules with identical linguistic values for the output that are known as non-monotonic rules. The filtration removes the redundant computations in the fuzzy inference with respect to the crisp values of the inputs to the fuzzy system. The method identifies the redundant rules after fuzzific...

Traditional forecasting methods need strict assumptions such as normality and linearity. It is very difficult to satisfy these assumptions for real-world time series. Many realworld time series can be easily analyzed by using fuzzy time series methods since fuzzy time series methods do not require any strict assumptions. Therefore, fuzzy time series approaches have been getting more and more at...

In this paper, a nonconventional structure for a “fuzzy” controller is proposed. It does not require signal division, and it produces control surfaces similar to classical fuzzy controllers. The structure combines fuzzification, MIN operators, normalization, and weighted sum blocks. The fuzzy architecture is implemented as a VLSI chip using 2m n-well technology. A new fuzzification circuit, whi...

Computing the output of fuzzy systems usually passes through three stages i.e. fuzzification, inference, and defuzzification. The computation of the output requires too many operations and considerable time. This apparently discourages the use of fuzzy systems in time-critical applications. In this paper, a new two-stage representation of fuzzy systems, which reduces the operations needed to co...