This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationar...

Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based...

The recognition of ten Thai isolated numerals from zero to nine and 60 Thai polysyllabic words are compared between different recognition techniques, namely, Neural Network, Modified Rackpropagation Neural Network. Fuzzy-Neural Network, and Hidden Markov Model. The I j-state left-to-right discrete hidden markov model in cooperation with the vector quantization technique has been studied and com...

In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to...

This paper presents a simulation study on Fuzzy Markov chains to identify some characteristics about their behavior, based on matrix analysis. Through experimental evidence it is observed that most of fuzzy Markov chains does not have an ergodic behavior. So, several sizes of Markov chains are simulated and some statistics are collected. Two methods for obtaining the Stationary Distribution of ...

In this paper, we formulate the fuzzy perceptive model for discounted Markov decision processes in which the perception for transition probabilities is described by fuzzy sets. The optimal expected reward, called a fuzzy perceptive value, is characterized and calculated by a new fuzzy relation. As a numerical example, a machine maintenance problem is considered.

In a continuous-time fuzzy stochastic system, a stopping model with fuzzy stopping times is presented. The optimal fuzzy stopping times are given under an assumption of regularity for stopping rules. Also, the optimal fuzzy reward is characterized as a unique solution of an optimality equation under a differentiability condition. An example in the Markov models is discussed.

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...

The fast and accurate forecasting method can help makers to make appropriate strategy. Zadeh was given the definition of a fuzzy set in 1965. Song and Chissom proposed the definition and the forecasting framework of fuzzy time series in 1993. Sullivan and Woodall first proposed the forecasting method to handle one factor with probability Markov model in 1994. Li and Cheng proposed a stochastic ...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...