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Abstract: It is commonly accepted by many researchers that multiple
evidence from different sources of different importance or reliability
are not equally important when they are combined according to Dempster-Shafer
theory, but it is seldom considered in the existent combination methods.
A new method is presented to solve this problem, by which the considered
evidence are first balanced according to the weighted average of all and
then combined. The method is incorporated into a neural network
classifier, which is based on Dempster-Shafer theory, to construct a
weighted evidence network and the network is applied to mechanical
equipment fault diagnosis problem in the followed experiments. The
experiment results demonstrate the excellent performance of this network
as compared to the improved RBF network; also the validity of the
proposed method in improving the combination’s accuracy of multiple
evidence is proved.
Key
words: Evidence theory Combination rules Neural network Pattern
recognition
CLC No: TH20
TP274.5
国家自然科学基金(59990472)和国家“九五”攀登计划(PD9521908Z1)资助项目.
Received 20010513, received in revised form 20020011026
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