Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Authors

  • Wen Jiang
  • Yan Yang Northwestern Polytechnical University School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
  • Yu Luo Northwestern Polytechnical University School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
  • Xiyun Qin Northwestern Polytechnical University School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Keywords:

data fusion, dempster-Shafer evidence theory, basic probability assignment (BPA), generalized fuzzy numbers, similarity measures

Abstract

Dempster-Shafer theory of evidence has been widely used in many data fusion application systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. The proposed method can overcome the drawbacks of the existing similarity measures. Then, we propose a new method for obtaining basic probability assignment (BPA) based on the proposed similarity measure method between generalized fuzzy numbers. Finally, the efficiency of the proposed method is illustrated by the classification of Iris data.

References

A. Dempster(1967), Upper and lower probabilities induced by multivalued mapping, Annals of Mathematical Statistics, ISSN 0003-4851, 38(2): 325-339.

G. Shafer(1976), A mathematical theory of evidence, Princeton University Press, ISBN 978- 069-11-0042-5.

Y.M. Zhu, L. Bentabet, M. Rombaut, O. Dupuis, V. Kaftandjian, D. Babot(2002), Automatic determination of mass functions in DS theory using FCM and spatial neighbourhood information for image segmentation, Optical Engineering, ISSN 0091-3286, 41(4): 760-770.

A. Bendjebbour, Y. Delignon, L. Fouque, V. Samson, W. Pieczynski(2001), Multisensor image segmentation using DS fusion in Markov fields context, IEEE Trans. Geosci. Remote Sensing, ISSN 0196-2892, 39(8): 1789-1798.

X. Guan, X. Yi, Y. He(2008), Study on algorithms of determining basic probability assignment function in Dempster-Shafer evidence theory, Proc. of the 7th Int. Conf. on Machine Learning and Cybernetics, 121-126.

B. Chen, J.F. Wang, S.B. Chen(2010), Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion, International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, 48(1-4): 83-94.

X.M. Li, L.X. Ding, Y. Li, G. Xu, J.B. Li(2009), HVAC fan mechinery fault diagnosis based on ANN and D-S evidence theory, IITA Int. Conf. on Control, Automation and Systems Engineering, Zhangjiajie, China, 603-606.

Z. Xu, M. Liu, G. Yang, N. Li(2009), Application of interval analysis and evidence theory to fault location, IET Electric Power Application, ISSN 1751-8660, 3(1): 77-84.

Z.Y. Zuo, Y.F. Xu, G.C. Chen(2009), A new method of obtaining BPA and application to the bearing fault diagnosis of wind turbine, Proc. of the 2009 Int. Symposium on Information Processing, Huangshan, China, 368-371.

W. Jiang, J.Y. Peng, Y. Deng(2011), A new method to determine BPA in evidence theory, Journal of Computers, ISSN 1796-203X, 6(6): 1162-1167.

Y. Deng, W. Jiang, R. Sadiq(2011), Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method, Expert Systems with Applications, ISSN 0957-4174, 38(1): 571-578.

Y. Deng, R. Sadiq, W. Jiang, S. Tesfamariam(2011), Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach, Expert Systems with Applications, ISSN 0957-4174, 38(12): 15438-15446.

Y. Deng, W. Jiang, X.B. Xu(2009), Determinging BPA under uncertainty environments and its application in data fusion, Journal of Electronics (China), ISSN 0217-9822, 26(1): 13-17.

W. Jiang, Y. Luo, X.Y. Qin, J. Zhan(2015), An Improved Method to Rank Generalized Fuzzy Numbers with Different Left Heights and Right Heights, Journal of Intelligent and Fuzzy Systems, Accepted.

S. M. Chen(1996), Foreword, New methods for subjective mental workload assessment and fuzzy risk analysis, Cybernetics and Systems: An International Journal, ISSN 0196-9722, 27(5): 449-472.

H.S. Lee(2002), Optimal consensus of fuzzy opinions under group decision making environment, Fuzzy Sets and Systems, ISSN 0196-9722, 132(3): 303-315.

S.J. Chen, S.M. Chen(2003), Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers, IEEE Transaction on Fuzzy Systems, ISSN 1063-6706, 11(1): 45-56.

S.H. Wei, S.M. Chen(2009), A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers, Expert Systems with Applications, ISSN 0957-4174, 36(1): 589-598.

S.R. Hejazi, A. Doostparast, S.M. Hosseini(2011), An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers, Expert Systems with Applications, ISSN 0957-4174, 38(8): 9179-9185.

W. Jiang, X. Fan, D.J. Duanmu, Y.Deng(2011), A Modified Similarity Measure of Generalized Fuzzy Numbers, 2011 Int. Conf. on Advanced in Control Engineering and Information Science, 2773-2777.

R.A. Fisher(1936), The use of multiple measurements in taxonomic problems, Annals of Eugenics, ISSN 1469-1809, 7(2): 179-188.

Published

2015-04-28

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.