AbstmdFuzzy logic is becoming popular in dealing with data analysis problems that are normally handled by statistical approaches or ANNs. The major limitation is the difficulty in building the fuzzy rules from a given set of input-output data. This paper proposed a technique to extract fuzzy rules directly from input-output pairs. It uses a self-organising neural network and association rules t...

Mining fuzzy rules is one of the best ways to summarize large databases while keeping information as clear and understandable as possible for the end-user. Several approaches have been proposed to mine such fuzzy rules, in particular to mine fuzzy association rules. However, we argue that it is important to mine rules that convey information about the order. For instance, it is very interesting...

In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose com...

In this paper, the Gaussian type quadrature rules for fuzzy functions are discussed. The errors representation and convergence theorems are given. Moreover, four kinds of Gaussian type quadrature rules with error terms for approximate of fuzzy integrals are presented. The present paper complements the theoretical results of the paper by T. Allahviranloo and M. Otadi [T. Allahviranloo, M. Otadi,...

Abstract: This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine. Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upp...

This paper proposes a fuzzy classifier system (FCS) using fuzzy rules given by hyper-cone membership functions. The hyper-cone membership function is expressed by a kind of radial basis function, and its fuzzy rules can be flexibly located in input and output spaces. Therefore, The FCS can generate excellent rules which have the best location and shape of membership functions. We apply the FCS ...

Some fuzzy expert systems have used fuzzy rules with numerical values which represent degrees of conndence for rules. We discuss two kinds of interpretations for these numerical degrees of conndence for rules, called "di-rect degrees " and "indirect degrees". Then, we apply Zadeh's, Baldwin's, and Tsukamoto's reasoning method to the rules under the two interpretations using general T-norms, and...

In this paper, we use fuzzy rule-based classification systems for classify cells of the Eimeria of Domestic Fowl based on Morphological Data. Thirteen features were extracted of the images of the cells, these features are genetically processed for learning fuzzy rules and a method reward and punishment for tuning the weights of the fuzzy rules. The experimental results show that our classifier ...

Fuzzy logic is a mathematical approach towards the human way of thinking and learning. Based on if-then rules, we can design fuzzy controllers with the intuitive experience of human beings. However, it is not practical for a designer to find necessary number of rules and determine appropriate parameters by hand. Hence, we incorporate a reinforcement learning method with basic fuzzy rules so tha...

This chapter discusses several issues related to the design of linguistic models with high interpretability using fuzzy genetics-based machine learning (GBML) algorithms. We assume that a set of linguistic terms has been given for each variable. Thus our modelling task is to find a small number of fuzzy rules from possible combinations of the given linguistic terms. First we formulate a threeob...