fuzzy logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. however, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the ...

This paper proposes a generation method of facial expressions using fuzzy measure and fuzzy integral for robotic heads. Human’s emotion state can be represented by a fuzzy measure which can effectively deal with ambiguity. Because facial expressions are usually ambiguous such that it is difficult to discern emotions and assign a sharp boundary to each emotion. In this method, users can adjust t...

In this paper controlled DC-DC converter using several fuzzy logic control law algorithms are studied. These controllers are designed to eliminate the overshoot and reduce the settling time response. In order to improve this property several control algorithms were investigated. The first one was the state controller with decomposed fuzzy PI controller and the second one was fuzzy PD state spac...

Since the T-S fuzzy system can approximate any nonlinear system with arbitrary accuracy, it is also expected to be a suitable approach to observe the states of a stochastic nonlinear system. Up to date, a few state estimators for stochastic T-S fuzzy systems have been proposed and applied to various fields without rigorous proof. In this paper, we first derive a sufficient condition based on th...

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic -means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fu...

This paper, deals with an indirect adaptive fuzzy control of discrete-time MIMO nonlinear systems with parametric variations. The synthesis of the state feedback control law is based on the T-S fuzzy models developed by a local description of the considered system. In the first step, gradient method is used to adjust model parameters locally estimated by the fuzzy model. The state feedback cont...

In this paper we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work in 2]. We solve this problem by deenining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy err...

This paper aims to develop a new approach to assess the duration of state in the DEVS formalism by fuzzy controller. The idea is to define a set of fuzzy rules obtained from observers or expert knowledge and to specify a fuzzy model which computes this duration, this latter is fed into the simulator to specify the new value in the model. In conventional model, each state is defined by a mean li...

Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...