A fusion diversity reception algorithm based on advanced D-S theory of evidence

Wang, Xuanming; Zhang, Xiaolin; Jing, Xiaojun; Zhang, Jie

doi:10.1109/icbnmt.2011.6155996

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2014

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“…As a new technology, neural network has the very strong learning and adaptive ability. In the field of fault diagnosis, it has been successfully applied [2][3][4]. Because of the influence of sample selection, D-S theory can solve the uncertain decision making diagnosis results conflict.…”

Section: Introductionmentioning

confidence: 99%

A Gas Pipeline Leakage Diagnosis of Fusing BP Neural Network Basing on WSN and D-S Theory

Liu

Gong

et al. 2014

AMM

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For reasons of low accuracy of artificial survey leakage, a gas pipeline leakage diagnosis method based on BP neural networks and D-S theory is presented by introducing WSN and information fusion theory. Two sub-neural networks are established at normal node to simplify network structure. The leakage characteristic parameters of negative pressure wave and acoustic emission signals are used as input eigenvector respectively for primary diagnosis. Through making preliminary fusion result s as the basic probability assignment of evidence, the impersonal valuations are realized. Finally, all evidences are aggregated at normal and sink node respectively by using the improved combination rules. The method makes full use of redundant and complementary leakage information. Numerical example shows that the proposed improves the leakage diagnosis accuracy and decreases the recognition uncertainty.

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Section: Introductionmentioning

confidence: 99%