نتایج جستجو برای: possibilistic fuzzy c
تعداد نتایج: 1141186 فیلتر نتایج به سال:
Existing standards for crisp description logics facilitate information exchange between systems that reason with crisp ontologies. Applications with probabilistic or possibilistic extensions of ontologies and reasoners promise to capture more information, because they can deal with more uncertainties or vagueness of information. However, since there are no standards for either extension, inform...
Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use probability models for cluster analysis. However, the corresponding probability distributions to most clus...
Linear systems with fuzzy parameters and crisp variables defined by the Zadehs extension principle are called possibilistic linear systems. The purpose of this paper is to outline some properties of these systems.
Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy c-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concep...
This paper analyzes a single-period inventory model of profit maximization with a reordering strategy in an imprecise environment. The entire period is divided into two slots and the customer demand is considered as a fuzzy number in situations where the demand in each slot is linguistic in nature and characterized as ‘demand is about d’. The reordering is to be done during the mid-season after...
Autonomous underwater vehicle (AUV) is one of the most important exploration tools in ocean environment, whose movement realized by thrusters, however, thruster fault happens frequently engineering practice. Ocean currents perturbations could produce noise for diagnosis, order to solve diagnostics, a possibilistic fuzzy C-means (PFCM) algorithm proposed realize classification this paper. On bas...
In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the possibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimators in feature space and showing high robustne...
The image segmentation performance of any clustering algorithm is sensitive to the features used and the types of object in an image, both of which compromise the overall generality of the algorithm. This paper proposes a novel fuzzy image segmentation considering surface characteristics and feature set selection strategy (FISFS) algorithm which addresses these issues. Features that are exploit...
| In this paper, we will present a framework for reasoning with vague and uncertain information by fuzzy truth-valued logics. It is shown that possibilistic logic, many-valued logic, and approximate reasoning can all be embodied in the uniform framework.
The fuzzy c-means (FCM) has been a well-known algorithm in machine learning/data mining area as a clustering algorithm. It can also be used for image segmentation, but the algorithm is not robust to noise. The possibilistic c-means (PCM) algorithm was proposed to overcome such a problem. However, the performance of PCM is too sensitive to the initialization of cluster centers, and often deterio...
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