Systems of fuzzy relation equations are considered as models of fuzzy inference systems. A proper use of an inference mechanism connected to a fuzzy relation modelling a fuzzy rule base is certified by keeping the fundamental interpolation condition. The paper aims at new solutions of systems of fuzzy relation equations and introduces new solvability criteria.

In this paper, a fuzzy web information retrieval system is developed. The system uses many of the tools and methods involved in fuzzy logic and fuzzy set theory, along with standard algorithms involving information retrieval on the web. The results of the ranking algorithm use the fuzzy relational BK-products, fuzzy thesauri, and fuzzy closure properties for purposes of retrieving relevant docu...

This paper shows the use of Fingrams –Fuzzy Inference-grams– aimed at unveiling graphically some hidden details in the usual behavior of the precise fuzzy modeling algorithm FURIA –Fuzzy Unordered Rule Induction Algorithm–. FURIA is recognized as one of the most outstanding fuzzy rule-based classification methods attending to accuracy. Although FURIA usually produces compact rule bases, with lo...

This report presents a generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of fuzzy controllers by supplying its a selforganizing capability. It is demonstrated that fuzzy controllers prepared to maintain control objects in the prescribed conditions are often fail to control when such a conditions are dramaticall...

The objective of this study is to present an offline control of highly non-linear inverted pendulum system moving on a plane inclined at an angle of 10° from horizontal. The stabilisation was achieved using three different soft-computing control techniques i.e. Proportional-integral-derivative (PID), Fuzzy logic and Adaptive neuro fuzzy inference system (ANFIS). A Matlab-Simulink model of the p...

A neural fuzzy system learning with linguistic teaching signals is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. At first, we propose a fivelayered neural network for the connectionist realization of a fuzzy inference system. Th...

Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy. Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be considered. Moreover Bayesian inference methods in the situation of fuzzy a-priori information and fuzzy da...

A spectral classification comparison was performed using four different classifiers; the parametric maximum likelihood classifier and three non-parametric classifiers; neural networks, fuzzy rules, and fuzzy neural networks. The input image data is a SPOT satellite image of the Otago Harbour near Dunedin, New Zealand. The SPOT image data contains three spectral bands in the green, red and visib...

quantitative assessment is the most important means to identify hazard potential and manage risk for an industrial process. the implement of quantitative assessment in the early stage will help to develop inherently safer process, eliminating the hazard and reduce the possibility of accidental chain events and the magnitude of consequences. in this paper, after reviewing the presently available...

Direct torque control (DTC) of Switched reluctance motor is known to have simple control structure with comparable performance to that of field oriented control techniques. However, the role of optimal selection of the voltage space vector is one of the weakest points in a conventional DTC drive. In this paper optimal selection of voltage space vectors is achieved using neuro fuzzy controller. ...