NDLI: Neural Evidence Fusion Model with Multi-source Information and Its Application

Content Provider	IEEE Xplore Digital Library
Author	Shouzhi Wei Ningde Jin
Copyright Year	2007
Description	Author affiliation: Northeastern Univ. at QinHuangDao, Qinhuangdao (Shouzhi Wei)
Abstract	The oilfield remaining oil distribution forecast is called world-level difficult problems by oil 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.
Starting Page	585
Ending Page	589
File Size	609648
Page Count	5
File Format	PDF
ISBN	1424412196
DOI	10.1109/ICIA.2007.4295800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-08
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuses neural evidence fusion model Predictive models Reliability engineering Floods Application software evidence fusion Distributed computing Subjective evidences and Objective evidences Petroleum Neural networks Cities and towns BP neural network combination Hydrocarbon reservoirs remaining oil distribution forecast
Content Type	Text
Resource Type	Article

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