A cover technique to verify the reliability of a model for calculating fuzzy probabilities

Chongfu Huang

International Journal of Disaster Risk Science ›› 2011, Vol. 2 ›› Issue (3) : 1 -10.

PDF
International Journal of Disaster Risk Science ›› 2011, Vol. 2 ›› Issue (3) :1 -10. DOI: 10.1007/s13753-011-0011-x
Article

A cover technique to verify the reliability of a model for calculating fuzzy probabilities

Author information +
History +
PDF

Abstract

Many models have been suggested to calculate fuzzy probabilities in risk analysis. In general, the reliability of a model is demonstrated by practical effects or proved theoretically. In this article we suggest a new approach called the cover technique to verify the model’s reliability. The technique is based on a hypothesis that a statistical result can approximately confirm a fuzzy probability as a fuzzy-set-valued probability. A cover is constructed by many biprobability distributions. The consistency degree of a cover and a fuzzy probability distribution is employed to verify the reliability of a model. We present a case that shows how to construct a distribution-cover and calculate the consistency degree of the cover and a possibility-probability distribution. A series of numerical experiments with random samples from a normal distribution verify the reliability of the interior-outer-set model.

Keywords

cover technique / fuzzy probability / histogram / interior-outer-set model / possibility-probability distribution

Cite this article

Download citation ▾
Chongfu Huang. A cover technique to verify the reliability of a model for calculating fuzzy probabilities. International Journal of Disaster Risk Science, 2011, 2(3): 1-10 DOI:10.1007/s13753-011-0011-x

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

de Cooman G.. A Behavioural Model for Vague Probability Assessments. Fuzzy Sets and Systems, 2005, 154(3): 305-58 10.1016/j.fss.2005.01.005

[2]

Dubois D., Prade H.. Fuzzy Sets, Probability, and Measurement. European Journal of Operational Research, 1989, 40(2): 135-54 10.1016/0377-2217(89)90326-3

[3]

de Finetti B.. La Prévision: ses Lois Logiques, ses Sources Subjectives. Annales de l’Institut Henri Poincaré, 1937, 7(1): 1-68.

[4]

Freeling A. N. S.. Fuzzy Sets and Decision Analysis. IEEE Transactions on Systems, Man and Cybernetics, 1980, SMC-10(7): 341-54 10.1109/TSMC.1980.4308515

[5]

Huang, C. F. 1998. Concepts and Methods of Fuzzy Risk Analysis. In Proceedings of the 1st China-Japan Conference on Risk Assessment and Management, 12–23, Beijing, China, 1998.

[6]

Huang C. F.. Demonstration of Benefit of Information Distribution for Probability Estimation. Signal Processing, 2000, 80(6): 1037-48 10.1016/S0165-1684(00)00018-9

[7]

Huang C. F.. An Application of Calculated Fuzzy Risk. Information Sciences, 2002, 142(1): 37-56 10.1016/S0020-0255(02)00156-1

[8]

Huang C. F.. Information Diffusion Techniques and Small Sample Problem. International Journal of Information Technology and Decision Making, 2002, 1(2): 229-49 10.1142/S0219622002000142

[9]

Huang C. F., Jia D. Y.. Histogram-Covering Approach to Verify Model Reliability. Journal of Multiple-Valued Logic and Soft Computing, 2008, 14(3–5): 265-75.

[10]

Huang C. F., Moraga C.. A fuzzy Risk Model and its Matrix Algorithm. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(4): 347-62 10.1142/S0218488502001521

[11]

Huang C. F., Ruan D.. Fuzzy Risks and an Updating Algorithm with New Observations. Risk Analysis, 2008, 28(3): 681-94 10.1111/j.1539-6924.2008.01057.x

[12]

Huang C. F., Shi Y.. Towards Efficient Fuzzy Information Processing—Using the Principle of Information Diffusion, 2002, Heidelberg: Physica-Verlag

[13]

Karimi I., Hülermeier E.. Risk Assessment System of Natural Hazards: A New Approach Based on Fuzzy Probability. Fuzzy Sets and Systems, 2007, 158(9): 987-99 10.1016/j.fss.2006.12.013

[14]

Moeller B., Beer M.. Fuzzy Randomness, 2003, Berlin: Springer

[15]

Moraga, C., and C. F Huang. 2003. Learning Subjective Probabilities from a Small Data Set. In Proceedings of the 33rd International Symposium on Multiple-Valued Logic, 355–60, Tokyo, Japan, 2003.

[16]

Otness R. K., Encysin L.. Digital Time Series Analysis, 1972, New York: John Wiley

[17]

Ramsey F. P.. Braithwaite R. B.. Truth and Probability. The Foundations of Mathematics, 1931, London: Routledge & Kegan Paul 156-98.

[18]

Tanaka H., Fan C., Toguchi K.. Fault Tree Analysis by Fuzzy Probability. IEEE Transactions on Reliability, 1983, R-32(5): 453-57 10.1109/TR.1983.5221727

[19]

Walley P.. Statistical Reasoning with Imprecise Probabilities, 1991, London: Chapman & Hall

[20]

Watson S. R., Weiss J. J., Donnell M. L.. Fuzzy Decision Analysis. IEEE Transactions on Systems, Man and Cybernetics, 1979, SMC-9(1): 1-9 10.1109/TSMC.1979.4310067

[21]

Williams P. M.. Notes on Conditional Previsions, 1975, UK: School of Mathematical and Physical Science, University of Sussex

[22]

Zadeh L. A.. Probability Measures of Fuzzy Events. Journal of Mathematical Analysis and Applications, 1968, 23(2): 421-27 10.1016/0022-247X(68)90078-4

[23]

Zadeh L. A.. Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1978, 1(1): 3-28 10.1016/0165-0114(78)90029-5

[24]

Zhang J. X.. Study on Theory and Method of New Generation of Natural Disaster Risk Zoning-Taking Earthquake Disaster as an Example, 2005, Beijing: Beijing Normal University

[25]

Zong, T. 2004. Improvement on Interior-Outer-Set Model for Estimating Fuzzy Probability. In Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society, 578–583. Banff, Canada, 2004.

PDF

169

Accesses

0

Citation

Detail

Sections
Recommended

/