We introduce a fuzzy controller which uses a fuzzy rule base diierently to the classical Mamdani approach. We argue that it has several desirable and well-motivated properties which often cannot be obtained by a Mamdani controller. Let X and Y denote the input and the output space, respectively, and let F(:) denote the collection of all fuzzy subsets. The support of a fuzzy set A 2 F(X) is Supp...

We describe an automatic approach for evaluating interpretability of fuzzy rule-based classifiers. The approach is based on the logical view of fuzzy rules, which are interpreted as rows in truth tables. These truth tables are subject of a minimization procedure based on a variant of the Quine-McCluskey algorithm. The minimized truth tables are used to build new fuzzy rules, which are compared ...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

Several Fuzzy Rule Interpolation (FRI) techniques have limitations from the direct application point of view, for example their applicability is limited to the one dimensional case, or they can be defined only based on the two closest surrounding rules of the actual observation. This is the reason why relatively few FRI methods can be found among the practical fuzzy rule based applications. Wit...

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...

Thanks to their ability to model natural language, fuzzy rules are very popular in expert knowledge representation. Mamdani fuzzy systems are widely used for process simulation or control. Nevertheless, fuzzy implicative rules, and especially gradual rules, provide another kind of knowledge representation, which can be very useful in approximate reasoning. In this paper, the two types of rules ...

A novel fuzzy controller is proposed here to improve the positioning precision of servo system of CNC lathes. To utilize the high–accuracy potential of the VUD fuzzy controller, a grating–ruler was used as a sensor to measure the displacement of work pieces. Designing the VUD fuzzy controller involved five steps, i.e., setting up universes of discourse and parameters, selecting membership funct...

This paper presents a genetic algorithm (GA) that automatically constructs the knowledge base used by fuzzy decision support systems (FDSS). The GA produces an optimal approximation of a set of sampled data from a very small amount of input information. The main interest of this method is that it can be used to automatically generate (without the help of an expert) a fuzzy knowledge base ± i.e....