Multiple Weighted Improved Exponential Bidirectional Associative Memory Model

CHEN Song-can;CAI Jun

ACTA ELECTRONICA SINICA ›› 2002, Vol. 30 ›› Issue (8) : 1200-1203.

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ACTA ELECTRONICA SINICA ›› 2002, Vol. 30 ›› Issue (8) : 1200-1203.
论文

Multiple Weighted Improved Exponential Bidirectional Associative Memory Model

  • CHEN Song-can1,2, CAI Jun1
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Abstract

C C Wang and coworkers built a belief combination model consisted of multiple exponential bidirectional associative memory (Multi-eBAM) through using eBAM with equal privileges and applied it into the voting decision making of multiple experts and obtained the latter decision-making performance.Using Chen improved eBAM (IeBAM) and endowing different privilege to each IeBAM or expert,a multiple weighted belief combination model consisted of IeBAM (Multi-WIeBAM) is constructed and investigated and becomes an extension to Wang Multi-eBAM model.Then its stability in synchronous and asynchronous updating modes is respectively proven and its corresponding decision-making performance and majority factor for different privilege of each expert are obtained.So the proposed model confirms the real-life voting decision.The initial analysis indicates that the majority factor of Multi-WIeBAM is tighter than that of the corresponding Multi-WeBAM,in other words,the former has better decision-making performance than the latter.Finally the experimental results also verify the above point.

Key words

decision making / multiple evidence reasoning / weighted / associative memory / neural networks

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CHEN Song-can;CAI Jun. Multiple Weighted Improved Exponential Bidirectional Associative Memory Model[J]. Acta Electronica Sinica, 2002, 30(8): 1200-1203.
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