State minimization is a fundamental problem in automata theory. The problem is also of great importance in the study of fuzzy automata. However, most work in the literature considered only state reduction of fuzzy automata, whereas the state minimization problem is almost untouched for fuzzy automata. Thus in this paper we focus on the latter problem. Formally, the decision version of the minim...

this research presents a dynamic mathematical system for modeling and simulating the quality management process in construction projects. through sets of cause and effect feedback loops, all factors that internally and externally affect the quality management process are addressed. the proposed system integrates fuzzy logic with system dynamics simulation scheme to consider the uncertainties as...

The probabilistic fuzzy set (PFS) and the related probabilistic fuzzy logic system (PFLS) is designed for handling the uncertainties in both stochastic and nonstochastic nature. In this paper, a bell-shaped probabilistic fuzzy set is proposed and the related PFLS is constructed and applied to a modeling problem to study stochastic modeling capability. It clearly discloses that the bell-shaped P...

For modeling of change of terminal server load, the approach including representation of time series of server parameters in the form of fuzzy time series is used. Further in the article the analysis of fuzzy time series is considered. The model of fuzzy tendencies is offered for terminal server traffic modeling. This model reflects change of the volume of terminal server traffic expressed ling...

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...