Theory of fuzzy integrals and its applications

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Trends in extreme learning machines

Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

Application of decision-making techniques in supplier selection: A systematic review of literature

Neighborhood rough set based heterogeneous feature subset selection

Rough sets and fuzzy sets have been proved to be powerful mathematical tools to deal with uncertainty, it soon raises a natural question of whether it is possible to connect rough sets and fuzzy sets. The existing generalizations of fuzzy rough sets are all based on special fuzzy relations (fuzzy similarity relations, T-similarity relations), it is advantageous to generalize the fuzzy rough sets by means of arbitrary fuzzy relations and present a general framework for the study of fuzzy rough sets by using both constructive and axiomatic approaches. In this paper, from the viewpoint of constructive approach, we first propose some definitions of upper and lower approximation operators of fuzzy sets by means of arbitrary fuzzy relations and study the relations among them, the connections between special fuzzy relations and upper and lower approximation operators of fuzzy sets are also examined. In axiomatic approach, we characterize different classes of generalized upper and lower approximation operators of fuzzy sets by different sets of axioms. The lattice and topological structures of fuzzy rough sets are also proposed. In order to demonstrate that our proposed generalization of fuzzy rough sets have wider range of applications than the existing fuzzy rough sets, a special lower approximation operator is applied to a fuzzy reasoning system, which coincides with the Mamdani algorithm.

On the generalization of fuzzy rough sets

Architecture selection for networks trained with extreme learning machine using localized generalization error model

On the handling of fuzziness for continuous-valued attributes in decision tree generation

Fuzzy linear regression analysis

Feature extraction is crucial for image recognition;the performance of image recognition is decided by the performance of image feature extraction.It is becoming a new hot area in the field of intelligent image processing to extract feature with excellent descriptive ability from original image.This review introduces the basic ideas of image feature extraction,their major characteristics and some current research trends.

Wang Xizhao

Papers

On the generalization of fuzzy rough sets

Architecture selection for networks trained with extreme learning machine using localized generalization error model

On the handling of fuzziness for continuous-valued attributes in decision tree generation

Fuzzy linear regression analysis

Research on the Image Feature Extraction