Evolutionary learning and especially genetic optimisation algorithms have recently received a lot of research attention as tools for identifying fuzzy models of the systems. Most often fuzzy modelling employ the fuzzy IF–THEN rules. In this paper, besides AND–operator the OR–operator is also considered in constructing the premise rule base. A genetic algorithm is utilised to find the premise st...

This paper describes a genetic programming approach to the construction of fuzzy classification system with if-then fuzzy rules. Recently many research studies were focusing on utilisation of evolutionary techniques for automatically extracting fuzzy rules from data. In this paper we present a method based on genetic programming with a special structure preserving representation and special rul...

We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. In this paper we describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of " normal behavior. " To detect anoma...

In the paper we are interested in the question of coherence of radial implicative fuzzy systems with nominal consequents (radial I-FSs with NCs). Implicative fuzzy systems are fuzzy systems employing residuated fuzzy implications for representation of IF-THEN structure of their rules. Radial fuzzy systems are fuzzy systems exhibiting the radial property in antecedents of their rules. The proper...

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classifica...

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...

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...