随机时差证据及海洋地震信息判定级融合问题的研究
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摘要
由于各自独立证据源采集信息的种类、时刻和周期都不同,组合时多元证据间时差呈随机性,若直接使用D-S组合规则,可能产生与知觉相悖的结果。文中使用时效函数衡量证据有效性与时间关系,并将其引进信任函数,用于地震信息判定级融合评估,仿真实验取得有意义结果。
Multiple independent sources always collect information at different time. So the traditional Dempster-Shafer theory is unadapted for the combination of multiple evidences from different time and their intervals are random. Time-effect function is defined to describe the changing credibility over time and joints in the belief function. Then it is applied to the marine earthquake information fusion at the determinant level. The simulation results are significant compared with the traditional D-S theory.
Multiple independent sources always collect information at different time. So the traditional Dempster-Shafer theory is unadapted for the combination of multiple evidences from different time and their intervals are random. Time-effect function is defined to describe the changing credibility over time and joints in the belief function. Then it is applied to the marine earthquake information fusion at the determinant level. The simulation results are significant compared with the traditional D-S theory.
引文
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[2]Xia Yan,Iyengar S S,Brener N E.Event driven integration reasoning scheme for handling dynamic threats in an unstructured environment.Artificial Intelligence[J].1997,95(1):169-186.
[3]Cheaito Ali,Lecours,Michel et al.Study of a modified Dempster-Shafer approach using an expected utility interval decision rule.The International Society for Optical Engineering[J].1999,37(19):34-42.
[4]Valin Pierre,Boily,David.Truncated Dempster-Shafer optimization and benchmarking[J].The International Society for Optical Engineering,2000,40(51):237-246.
[5]Pigeon Luc,Solaiman Bassel,Toutin Thierry et al.Dempster-Shafer theory for multi-satellites remotely-sensed observations[J].The International Society for Optical Engineering,2000,40(51):228-236.
[6]Mufti Muid,Vachtsevanos George.Intelligent approach to fault detection and identification[J].In Proc of the American Control Conference1995,California,IRTTS:1621-1622.
[7]Vila M A,Delgado M,Gomez-Skarmeta A F.Pattern recognition with evidential knowledge[J].International Journal of Intelligent Systems,14(2):145-164.
[8]Suh Doug Young,Mersereau Russell M,Pettigrew Roderic I.et al.Knowledge-based system for boundary detection of four-dimensional cardiac magnetic resonance image sequences[J].IEEE Transactions on Medical Imaging,1993,12(1):65-72.
[9]Kawade,Masato.Object recognition system in a dynamic environment[R].In Proc of International Joint Conference of the4th IEEE International Conference on Fuzzy Systems[C].2002,IEEE,Piscataway,NJ,USA:1285-1290.
[10]Golshani Forouzan.Cortes-Rello Enrique.Dynamic route planning with uncertain information[J].Knowledge-Based Systems,1996,9(4):223-232.
[11]Dencoux Thierry.Analysis of evidence-theoretic decision rules for pattern classification[J].Pattern Recognition.1997,30(7):1095-1107.
[12]Cuzzolin Fabio,Frezza Ruggero.Evidential reasoning framework for object tracking[J].The International Society for Optical Engineering38(40):13-24.
[13]方银霞,申屠海港,宋海斌.海底天然气水合物的地震信息处理与分析[J].海洋通报,2001,.20(1):78-82.
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