Neural evidence fusion model with multi-source information and its application

被引:0
|
作者
Wei, Shouzhi [1 ]
Jin, Ningde [2 ]
机构
[1] Northeastern Univ, Dept Comp Engn, Qinhuangdao, Peoples R China
[2] Tianjin Univ, Sch Elect Engn & Automat, Tianjin, Peoples R China
来源
2007 INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2 | 2007年
关键词
subjective evidences and objective evidences; evidence fusion; BP neural network combination; neural evidence fusion model; remaining oil distribution forecast;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The oilfield remaining oil distribution forecast is called world-level difficult problems by off domain specialists in the world. The source of low forecast correctness are only consider objective evidences or subjective evidence, so the forecast results still exist limitation, it result in low accuracy, reliability and so on to identify the classification characteristics and to compute quantitative parameters. So, how to fuse all objective evidences and subjective evidences is a key problem to research remaining oil distribution. A new model is proposed, it fused BP neural networks combination models and two-level D-S evidence reasoning models, the exact classification results are implemented about many remaining oil distribution characteristics. The classification output reliability of each BP network and the reasoning result reliability of each domain fuzzy expert system are regarded as basic probability assignment of input evidence in D-S evidence reasoning model. The model has applied successfully in Daqing Oilfield of China.
引用
收藏
页码:586 / +
页数:2
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