In this paper, we present a fuzzy logic integration to the Hidden Markov Models (HMM). We have replaced the basic arithmetic operators by some adequate k z y operators. Using fuzzy operators permits us to relax the additivity constraint of probability measures. So we will need only the monotonicity with respect to set inclusion. Some results show the interest of our approach.

In the present paper, the study of fully fuzzy linear fractional programming problem (FFLFPP) using graded mean integration representation method is discussed where all the parameters and variables are characterized by trapezoidal fuzzy numbers. A computational algorithm has been presented to obtain an optimal solution by applying simplex method. To demonstrate the applicability of the proposed...

Sophisticated uncertainty representation and reasoning are necessary for the alignment and integration of Web data from different sources. For this purpose the extension of the Description Logics using fuzzy set theory has been proposed, resulting to fuzzy Description Logics (DLs). However, despite the fact that since the initial proposal a lot of work has been done in the area, the practicabil...

This paper presents a software tool (Qua.Si. III) for the simulation of continuous dynamical systems whose parameters and/or initial conditions are modelled by fuzzy distributions. The tool, which is the last version of the qualitative simulator Qua.Si. (Bonarini, Bontempi, 1994a), implements a new approach to the numerical integration of fuzzy dynamical systems, where the problem of propagatin...

This paper introduces Fuzzy State Automata for control applications. They are derived from the integration of traditional nite state automata and neuro-fuzzy systems, where a nite state automaton tracks the state of the plant under control and modiies the characteristic of the neuro-fuzzy system accordingly. The main diierence with respect to existing systems is that the states of the automata ...

In this paper, the theoretical foundations of generalised fuzzy Bayesian Network based on Vectorial Centroid defuzzification is introduced. The extension of Bayesian Network takes a broad view by examples labelled by a fuzzy set of attributes, instead of a classical set. Combining fuzzy set theory and Bayesian Network’s knowledge allows the use of fuzzy variables or attributes that widely used ...

In this paper, the Kapur cross-entropy minimization model for portfolio selection problem is discussed under fuzzy environment, which minimizes the divergence of the fuzzy investment return from a priori one. First, three mathematical models are proposed by defining divergence as cross-entropy, average return as expected value and risk as variance, semivariance and chance of bad outcome, respec...

An approach to distance sensor data integration that obtains a robust interpretation of the robot environment is presented in this paper. This approach consists in obtaining patterns of fuzzy distance zones from sensor readings; comparing these patterns in order to detect non-working sensors; and integrating the patterns obtained by each kind of sensor in order to obtain a final pattern that de...

This paper presents a new distance relay technique for transmission line protection by using well known control technique; Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS can be viewed either as a fuzzy system, a neural network or fuzzy neural network FNN. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to sim...

In the development of the wireless communication of the future, mobility management and existing heterogeneous wireless networks integration in next generation or 4G wireless networks are developing trend. Such an environment poses a significant challenge: how can we choose an appropriate Network Access Point (NAP) to achieve load balance and offer high quality communication in heterogeneous wi...