Received 20 April 2010; revised 14 August 2010; accepted 29 August 2010. ———————————————————————————————Abstract In this paper, we present a numerical method for solving fully fuzzy polynomials. The proposed method is based on approximating fuzzy neural network. This method can also lead to improving numerical methods. In this work, an architecture of fuzzy neural networks is also proposed to f...

A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters...

Fermentation process is vital and important in many biotechnological applications. However modeling the fermentation process is considered a challenging and complex problem. The complexity of the problem is driven by the need of efficient, accurate, not expensive, and reliable predictive models. In this paper, we apply a Takagi-Sugeno Fuzzy Logic technique for modeling the lipase activity produ...

The use of fuzzy control systems had been proved to be very useful for industrial applications. Fuzzy modeling is an area where an overwhelming number of techniques for non-linear function approximation have been developed but there is a lack of handy information for the end user in industry. The present paper deals with this topic by helping the end user with issues like when and how to use fu...

A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Forming the crucial point in fuzzy modeling, the fuzzy model identiication procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, to provide the parameters of the fuzzy model. To enhance the eeciency of the fuzzy mode...

A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Representing the crucial point in fuzzy modeling, the fuzzy model identiication procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, providing the parameters of the fuzzy model. To enhance the eeciency of the fuzzy ...

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (via fuzzy sets and fuzzy rule bases). The FLEXFIS and e...

We discuss the role of fuzzy sets in modeling business rules. The technology involved in fuzzy systems modeling is described. We next introduce some ideas from the Dempster-Shafer theory of evidence. We use the Dempster-Shafer framework to provide a machinery for including randomness in the fuzzy systems