This paper presents a method of using the so-called „bacterial algorithm” [4, 5] for extracting the fuzzy rule base from a training set. The class of membership functions is restricted to trapezoidal, as it is general enough and widely used. The pseudobacterial genetic algorithm (PBGA) is show. The PBGA optimises the trapezoidal membership functions in the rules by the bacterial mutation operat...

Fuzzy clustering algorithms like the popular fuzzy cmeans algorithm (FCM) are frequently used to automatically divide up the data space into fuzzy granules (fuzzy vector quantization). In the context of fuzzy systems, in order to be intuitive and meaningful to the user, the fuzzy membership functions of the used linguistic terms have to fulfill some requirements like boundedness of support or u...

This work constructs the membership functions of the system characteristics of a batch-arrival queuing system with multiple servers, in which the batch-arrival rate and customer service rate are all fuzzy numbers. The  -cut approach is used to transform a fuzzy queue into a family of conventional crisp queues in this context. By means of the membership functions of the system characteristics, ...

In this article, a real-coded genetic algorithm (GA) is proposed capable of simultaneously optimizing the structure of a system (number of inputs, membership functions and rules) and tuning the parameters that define the fuzzy system. A multideme GA system is used in which various fuzzy systems with different numbers of input variables and with different structures are jointly optimized. Commun...

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are k...

We propose two methods for tuning membership functions of a kernel fuzzy classifier based on the idea of SVM (support vector machine) training. We assume that in a kernel fuzzy classifier a fuzzy rule is defined for each class in the feature space. In the first method, we tune the slopes of the membership functions at the same time so that the margin between classes is maximized under the const...

Abstract : Fuzzy Logic is based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not only 0 or 1, as in crisp set theory. The degree of membership function is defined as the gradation in the extent to which an element is belonging to the relevant sets. Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for...

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 ...

Fuzzy controller’s design depends mainly on the rule base and membership functions over the controller’s input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechan...

A descriptive Takagi-Sugeno fuzzy rule based system suffers under the curse of dimensionality since the number of rules is equal to a fuzzy system with a fully filled up decision table. By defining high interest areas as areas where the input space is covered with many membership functions to achieve an acceptable error, the consequence is an often inappropriate fine grid of membership function...