In this dissertation the generation and tuning of fuzzy membership function parameters are considered as a part of the fuzzy model development process. The automatic generation and tuning of fuzzy membership function parameters are needed for the fast adaptation and tuning of fuzzy models of various nonlinear dynamical systems. The developed methods are especially useful in automatic fuzzy memb...

Predicting air pollution is an important prerequisite for estimating, monitoring and mapping unknown pollution values. We can use fuzzy spatial prediction techniques to determine pollution concentration areas in practical situations where our observations are imprecise and vague. Fuzzy membership kriging with a semi-statistical membership function is an example of this type of technique. The im...

The present study considered a fuzzy natural color system (NCS) in which triangular pyramid membership functions are constructed on the NCS color triangle. This system can process a fuzzy input to an NCS system and output to a center of gravity of three weights associated with respective grades. Triangular membership functions are applied to the hue, and triangular pyramid membership functions ...

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the app...

Most approaches for the data-based design of fuzzy rule-based classi ers (e. g. [1{4]) require a manual de nition of membership functions (MBFs). In large applications, this task is very diÆcult and time-consuming for human experts. As a practical application, a possible scenario for a manual design of membership functions in a medical expert system to support gait analysis is demonstrated (Sec...

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper,...

This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are...

Ant Colony Systems (ACS) have been successfully applied to different optimization issues in recent years. However, only few works have been done by employing ACS method to data mining. This paper addresses the lack of investigations on this study by proposing an ACS -based algorithm to extract membership functions in fuzzy data mining. In this paper, the membership functions were encoded into b...

In almost every work on fuzzy sets, the existence of membership functions taking part in the considered model is assumed and it is not studied in depth whether or not such functions exist. On the other hand, generally the relationship between a certain studied characteristic and its referential set is not problematic since it is usually a matter of direct measurement. However, in a great variet...

A clonal selection algorithm (CLONALG) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. In this study, a new method is proposed for optimization of the Multiple Input Single Output (MISO) fuzzy membership functions using CLONALG. The most appropriate placement of membership functions with respect to fuzzy variab...