Fuzzy control is a methodology that translates “if”-“then” rules Aj1(x1) & ... & Ajn(xn) → Bj(u) formulated in terms of a natural language, into an actual control strategy u(x⃗). Implication of uncertain statements is much more difficult to understand than “and”, “or”, and “not”. So, the fuzzy control methodologies usually start with translating “if”-“then” rules into statements that contain onl...

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....

This paper brieey reviews techniques for learning fuzzy rules. In many applications fuzzy if-then rules are not interpreted as implications , but in a procedural way that corresponds to a conjunction or Cartesian product. For this type of rules many approaches to rule learning have been proposed. However, for more complex fuzzy systems based on logical implications, there is still a need for su...

Classification rules are an important tool for discovering knowledge from databases. Integrating fuzzy logic algorithms into databases allows us to reduce uncertainty which is connected with data in databases and to increase discovered knowledge’s accuracy. In this paper, we analyze some possible variants of making classification rules from a given fuzzy decision based on cumulative information...

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for simi...

This paper proposes a fusion model to reinforce fuzzy association rules, which contains two main procedures: (1) employing the cumulative probability distribution approach (CPDA) to partition the universe of discourse and build membership functions; and (2) using the AprioriTid mining algorithm to extract fuzzy association rules. The proposed model is more objective and reasonable in determinin...

Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema [8]. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system ...

In this paper, we present a new approach for extracting fuzzy rules from numerical input–output data for pattern classification. The approach combines the merits of the fuzzy logic theory, and neural networks. The proposed approach uses rapid back propagation neural network (RBPNN), which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The network can be regarde...

This work describes experiments carried out using fuzzy formal concept analysis for the generation of fuzzy classification rules to be used by a genetic process. These rules are simply the intention of the formal concepts extracted from a fuzzy-based formal context. The motivation we have is the need for a method to generate fuzzy classification rules to be used as the search space of the genet...

This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been intensively applied in medical imaging. But these rules are app...