The fuzzy numbers arise in decision making, control theory, fuzzy systems and approximate reasoning problems. The operations on them are becoming more and more popular. The aim of this paper is to present the fuzzy arithmetic operations on fuzzy numbers in a new way, using the horizontal membership functions (HMFs). The horizontal membership functions enable to introduce uncertain, interval or ...

This paper presents a fuzzy, non-linear similarity measure designed for a clinical casebased reasoning system in radiotherapy treatment planning. The developed fuzzy similarity measure takes into account the distribution of attribute similarity values in the case base to ensure that the numerical values of the similarity between individual attributes are comparable and can be combined to give t...

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy s...

This paper deals with the simplest fuzzy PID controllers which employ two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. Mathematical model for a fuzzy PID controller is derived by using asymmetric Γ-function type and L-function type membership functions for each input, asymmetric trapezoidal membership functions for output, algebraic product trian...

This paper describes a new approach to model fuzzy sets using object-oriented programming techniques. Currently, the most frequently used method to model fuzzy sets is by using a pair of arrays to represent the set of ordered pairs, elements and their grades of membership. For continuous fuzzy sets, it is impossible to use infinite number of elements to model, therefore a limited number of arra...

Combining the results of multiple sensors can provide more accurate information than using single sensor. In this paper, we develop fuzzy clustering approach to data association and track fusion in multisensor multi-target environment. The proposed approach uses the fuzzy clustering means algorithm to get the degree of membership of new tracks to existing tracks. Unlike existing approaches, in ...

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules ar...

|Fuzzy information processing systems start with expert knowledge which is usually formulated in terms of words from natural language. This knowledge is then usually reformulated in computer-friendly terms of membership functions, and the system transform these input membership functions into the membership functions which describe the result of fuzzy data processing. It is then desirable to tr...

Fuzzy sets are naturally ordered by the subsethood relation A ⊆ B. If we only know which set which fuzzy set is a subset of which – and have no access to the actual values of the corresponding membership functions – can we detect which fuzzy sets are crisp? In this paper, we show that this is indeed possible. We also show that if we start with interval-valued fuzzy sets, then we can similarly d...

The fuzzy regression control chart is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this study, the theoretical structure of the “α-level fuzzy midrange for α-cut fuzzy X ~ -regression control chart” is proposed for triangular (TFN) and trapezoidal (TrFN) membership functions. In addition, the real w...