Multiple Weighted Improved Exponential Bidirectional Associative Memory Model

CHEN Song-can; CAI Jun

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Multiple Weighted Improved Exponential Bidirectional Associative Memory Model

更新时间：2025-07-16

- Multiple Weighted Improved Exponential Bidirectional Associative Memory Model
- Acta Electronica Sinica Vol. 30, Issue 8, Pages: 1200-1203(2002)
- 作者机构：
  
  1. 南京航空航天大学计算机科学与工程系,江苏,南京,210016
  2. 南京大学计算机软件新技术国家重点实验室,江苏,南京,210093
  3. 南京航空航天大学计算机科学与工程系江苏南京,210016
  4. 南京大学计算机软件新技术国家重点实验室江苏南京,210093
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Published：2002
- 稿件说明：
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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.
DOI：

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

摘要

C C Wang等作者利用指数双向联想记忆模型(eBAM)

构造了由多个eBAM构成的多重eBAM(Multi-eBAM)信念组合模型

使之可模拟多个专家的投票表决决策

并获得了Multi-eBAM在各eBAM具有同等权威度条件下的决策性能.本文在此基础上

通过对各eBAM引入不同的权值来模拟各专家不同的权威度

推广了Multi-eBAM.进一步借助陈所提出的改进型eBAM(IeBAM)

构建了相应的多重加权改进型eBAM(Multi-WIeBAM)信念组合模型

获得了此推理模型在同、异步方式下的决策性能及多专家不同权威度下的多数投票因子

使之更符合实际的多数表决决策.理论分析表明Multi-WIeBAM所获得的多数投票因子优于Multi-WeBAM的多数投票因子

即前者较后者具有更紧致的下界.实验结果也表明了Multi-WIeBAM的性能要优于Multi-WeBAM.

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.

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