A Possibility Estimation Model and its Application

Abstract:

Aiming at some uncertainty problems such as quality inspection of adhesive structure and risk assessment in the practical engineering application, a possibility estimation model is established. Firstly, according to the fuzziness, randomness and uncertainty of the measurement data, a transformation method of possibility distribution with non-single peak values and nonlinearity is proposed from probability density function. Secondly, for possibility distributions of measurement data of each sensor, a kind of possibility fusion rules is put forward, then the fusion distribution is estimated by the possibility mean. Finally the model is applied to the mechanical property estimation of adhesive structure, and the result forecasts the quality. The proposed model with strong applicability, not only provides convenience for the operations among possibility distributions, but also offers new ideas and new methods to deal with uncertain problems.

• Periodical: Applied Mechanics and Materials (Volumes 475-476)
• Pages: 423-427
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.475-476.423
• Online since: December 2013
• Authors: Lin Na Ji*, Feng Bao Yang, Xiao Xia Wang
• Keywords: Possibility Distribution, Possibility Estimation Model, Possibility Fusion Rule, Possibility Mean

References

[1] Dasarathy B.V. Intrusion detection[J]. Information Fusion, 2003, 4(4): 243-245.

[2] Appriou. A, Ayoun. A, etal. Fusion: general concepts and characteristics[J]. International Journal Intelligent Systems, 2001, 16(10): 1107-1134.

[3] Dongliang Peng, Chenglin Wen. Multi-sensor information and multiple source fusion theory and the applications[M]. Beijing: Science Press, 2010. (in Chinese).

[4] Zadeh L.A. Fuzzy sets as a basis for possibility theory [J]. Fuzzy Sets and Systems, 1978, 1(1): 3–28.

DOI: 10.1016/0165-0114(78)90029-5

[5] Dubois,D. Prade,H. Possibility Theory and its Applications: A Retrospective and Prospective View[J]. The 12th IEEE International Conference on Fuzzy Systems, 2003, 5(1): 5–11.

DOI: 10.1109/fuzz.2003.1209314

[6] R.C. Silva,A. Yamakami. The use of possible theory in the definition of fuzzy pareto-optimality[J]. Fuzzy optim Decis Making. 2011, 3(10): 11-30.

DOI: 10.1007/s10700-010-9092-z

[7] Delmotte, F. Detection of Defective Sources in the Setting of Possibility Theory[J]. Fuzzy Sets and Systems, 2007, 5(158): 555–571.

DOI: 10.1016/j.fss.2006.10.027

[8] Dubois,D. Prade,H. Rules and meta-rules in the framework of possibility theory and possibilistic logic[J]. Scientia Iranica, 2011, 18(3): 566-573.

DOI: 10.1016/j.scient.2011.04.008

[9] Yanhua Hou, Bo Yang. Research on possibility distribution construction methods under the uncertain cognition conditions[D]. Chengdu: University of Electronic Science and Technology of China, 2010. (in Chinese).

[10] Jamison.K. D, Lodwick W.A. The construction of consistent possibility and necessity measures[J]. Fuzzy sets and Systems, 2002, 132: 65-74.

DOI: 10.1016/s0165-0114(02)00047-7