In this paper an autonomous feature clustering framework has been proposed for performance and reliability evaluation of an environmental sensor network. Environmental time series were statistically preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans...

This report discusses how soft discretization can be implemented to train a discrete Bayesian Network directly from continuous data. The method consists of a soft discretization step that converts the continuous variables of the training cases into soft evidence, followed by a suitable parameter learning algorithm for the Bayesian Network. The learning algorithm is a modification of the Maximum...

This paper is a reply to Laviolette and Seaman’s critical discussion of fuzzy set theory. Rather than questioning the interest of the Bayesian approach to uncertainty, some reasons why Bayesian find the idea of a fuzzy set not palatable are laid bare. Some links between fuzzy sets and probability that Laviolette and Seaman seem not to be aware of are pointed out. These links suggest that, contr...

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

The main purpose of this paper is to provide a methodology for discussing the fuzzy. Bayesian system reliability from the fuzzy component reliabilities, actually we discuss on the Fuzzy Bayesian system reliability assessment based on Pascal distribution, because the data sometimes cannot be measured and recorded precisely. In order to apply the Bayesian approach, the fuzzy parameters are assume...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...

Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy. Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be considered. Moreover Bayesian inference methods in the situation of fuzzy a-priori information and fuzzy da...