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Determine Discounting Coefficient in Data Fusion Based on Fuzzy ART Neural Network

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

The method of discounting coefficient is an efficient way to solve the problem of evidence conflicts. In this paper a new method to calculate the discounting coefficient of evidence based on evidence clustering by the way of fuzzy ART neural network is proposed. The discounted evidence is taken into account in belief function combination. A numerical example is shown to illustrate the use of the proposed method to handle conflicting evidence.

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References

  1. Shafer, G.A.: Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  2. Dempster, A.P.: Upper and Lower Probabilities induced by a Multivalued Mapping. Annals of Mathematical Statistics 38, 325–339 (1967)

    Article  MATH  MathSciNet  Google Scholar 

  3. Liau, C.J., Lin, B.I.: Possibilistic Reasoning-a mini-survey and uniform Semantics. Artificial Intelligence 88(1), 163–193 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  4. Murphy, R.R.: Dempster-Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE Transaction on Robotics and Automation 14(1), 197–206 (1998)

    Article  Google Scholar 

  5. Pohl, C., Van Genderen, J.L.: Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications. International Journal of Remote Sensing 19(1), 823–854 (1998)

    Article  Google Scholar 

  6. Beynon, M., Curry, B., Morgan, P.: The Dempster-Shafer Theory of Evidence: An Alternative Approach To Multicriteria Decision Modeling. Omega 28(1), 37–50 (2000)

    Article  MathSciNet  Google Scholar 

  7. Zouhal, L.M., Denoeux, T.: An Evidence-theoretic K-NN Rule with Parameter Optimization. IEEE Transactions on Systems, Man and Cybernetics 28(1), 263–271 (1998)

    Google Scholar 

  8. Zadeh, L.: A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination. AI Magazine 7(2), 85–90 (1986)

    Google Scholar 

  9. Yager, R.R.: On the Dempster-Shafer Framework and New Combination Rules. Information Sciences 41(1), 93–138 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  10. Dubois, D., Prade, H.: Representation and Combination of Uncertainty with Belief Functions and Possibility Measures. Computational Intelligence 4(1), 244–264 (1998)

    Google Scholar 

  11. Smets, P.: The Combination of Evidence in the Transferable Belief Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 447–458 (1990)

    Article  MathSciNet  Google Scholar 

  12. Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligence 66(1), 191–234 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  13. Smets, P.: Resolving Misunderstandings about Belief Functions. International Journal of Approximate Reasoning 6(1), 321–344 (1992)

    Article  MATH  Google Scholar 

  14. Lefevre, E.: Belief Function Combination and Conflict Management. Information Fusion 3(1), 149–162 (2002)

    Article  Google Scholar 

  15. Audun, J.: The Consensus Operator For Combining Beliefs. Artificial Intelligence 141(1), 157–170 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  16. Haenni, R.: Comments on ”About the Belief Function Combination and The Conflict Management Problem” Real Alternatives. Information Fusion 3(1), 237–239 (2002)

    Article  Google Scholar 

  17. Murphy, C.K.: Combining Belief Functions When Evidence Conflicts. Decision Support Systems 29(7), 1–9 (2000)

    Article  Google Scholar 

  18. Yong, D., Shi, W.K., Zhu, Z.F.: Combining Belief Functions Based on Distance of Evidence. Decision support system 38(1), 489–493 (2004)

    Google Scholar 

  19. Sally, M., Bryan, S.: Using Evidence Theory for The Integration of Distributed Databases. International Journal of Intelligent System 12(1), 763–776 (1997)

    Article  Google Scholar 

  20. Zadeh, L.A.: A Simple View of the Dempster-Shafer Theory of Evidence and its Implication for the Rule of Combination. AI Magazine 7(1), 85–90 (1986)

    Google Scholar 

  21. Carpenter, G.A., Stephen, G.: Fuzzy ART Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4(1), 759–771 (1991)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department 8, Institute of Automatic Detection (810), Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Min Hang, Shanghai, China

    Dong Sun & Yong Deng

Authors
  1. Dong Sun
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  2. Yong Deng
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, D., Deng, Y. (2006). Determine Discounting Coefficient in Data Fusion Based on Fuzzy ART Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_191

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  • DOI: https://doi.org/10.1007/11759966_191

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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