System modeling in dynamic environments needs processing of streams 1 of sensor data and incremental learning algorithms. This paper suggests an incre2 mental granular fuzzy rule-based modeling approach using streams of fuzzy inter3 val data. Incremental granular modeling is an adaptivemodeling framework that uses 4 fuzzy granular data that originate from unreliable sensors, imprecise perceptio...

This study addresses the problem of modeling physiological systems using fuzzy logic. As an application we consider the problem of modeling the anesthetics process. In particular we consider the problem of modeling the evolution of the bispectral index in patients undergoing anesthesia with intravenous propofol. An identification based on fuzzy inference system is proposed. One of the main adva...

In this paper a fuzzy autoregressive (AR) model described in [1] is used to model and predict highspeed network traffic. This model approximates a complex nonlinear time-variant process by combining linear local autoregressive processes using a fuzzy clustering algorithm. We propose a method to estimate the traffic effective bandwidth at regular intervals, assuming the network traffic can be de...

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 ...