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...

The objective of this paper is to present a technique for order preference by similarity to ideal solution (TOPSIS) algorithm to linear fractional bi-level multi-objective decision-making problem. TOPSIS is used to yield most appropriate alternative from a finite set of alternatives based upon simultaneous shortest distance from positive ideal solution (PIS) and furthest distance from negative ...

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of...

A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of m...

|Fuzzy Logic Controllers constitute knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate the human knowledge into their knowledge base. The deenition of fuzzy rules and fuzzy membership functions is actually aaected by subjective decisions, having a great innuence over the whole FLC performance. Some eeorts have been made to obtain an improvement on sys...

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership funct...

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. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the...

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 transl...

Defuzzifier circuit is one of the most important parts of fuzzy logic controllers that determine the output accuracy. The Center Of Gravity method (COG) is one of the most accurate methods that so far been presented for defuzzification. In this paper, a simple algorithm is presented to generate triangular output membership functions in the Mamdani method using the multiplier/divider circuit and...

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of su...