The decision-making properties of discrete multiple exponentialbidirectional associative memories

A method for modeling the learning of belief combination in evidential reasoning using a neural network is presented. A centralized network composed of multiple exponential bidirectional associative memories (eBAM's) sharing a single output array of neurons is proposed to process the uncertainty management of many pieces of evidence simultaneously. The stability of the proposed multiple eBAM network is proved. The sufficient condition to recall a stored pattern pair is discussed. Most important of all, a majority rule of decision making in presentation of multiple evidence is also found by the study of signal-noise-ratio of multiple eBAM network. A guaranteed stable state condition, i.e., the condition for the fastest recall of a pattern pair, is also studied. The result is coherent with the intuition of reasoning.     

多重改进型指数双向联想记忆模型及其在多证据推理中的决策性能

提出了多证据推理中采用神经网络来模拟信念组合学习方法。网络由多个改进型指数双向联想记忆模型（IeBAM）构成,并且共享一个输出来同时进行多证据不确定性的管理。文中证明了多重IeBAM(Multi-IeBAM）的稳定性,讨论了在多条证据同时提交网络后的多数规则。理论和实验都证明了多数因子比Wang所提模型更紧凑、更严格,从而可保证在受一定程度的干扰下,专家们仍能做出正确决策。最后所给出的模拟例子的结果与直觉推理相吻合。     

Evidence combination rule with contrary support in the evidential reasoning approach

Three aspects of problems such as reasonable weight constraint, cumulative weight effect and relative weight equivalence cannot be well reflected in the evidential reasoning (ER) approach. In order to solve the above problems, a contrary support is defined to restrict the degree influenced by the evidence to be combined in combination process. Then pair-weighted and cumulative pair-weighted discounting methods are presented to generate basic probability assignment (BPA) for evidence. Pair-wised and recursive combination rules are established to make combination with the BPA of evidence by orthogonal sum operation and several theorems such as relative weight equivalence are proved. A combination algorithm is proposed to solve multiple criteria decision making (MCDM) problems by integrating pignistic probability and expected utility with the established combination rules. Finally, an illustrative example is provided to demonstrate the applicability of the proposed combination rules and algorithm.     

主客观证据融合模型及其应用研究

油田剩余油分布预测被国内外石油领域专家公认为世界难题,目前,其预测准确率低的根源在于或者只考虑部分客观证据、或者只考虑部分主观证据,导致对剩余油分布水淹类型等特征分类准确率低、可靠性差。所以,如何对来自多专业领域不同层次的全部客观证据及领域专家长期积累的主观证据进行融合,成为剩余油分布研究的核心问题。文章通过BP神经网络联合模型与两级D-S证据推理模型的优势互补进行主客观证据融合,实现了剩余油分布多属性特征的准确分类。提出了将BP神经网络分类结果的可信度及专家系统推理结论的可信度作为D-S证据推理模型输入证据基本概率赋值的有效方法。为各类多源信息融合系统的研究和工程实现提供了示例、途径和有益的经验。     

网络安全态势感知中D-S证据理论冲突证据的修正研究

D-S证据理论是不确定推理的一种重要方法,在许多方面都得到了广泛的应用。针对D-S证据理论在网络安全态势感知的数据融合过程中的应用,就多源数据的证据组合结果与直觉相悖的问题进行深入研究,提出了一种新的解决方案。该方案通过支持度的思想对冲突证据源进行修正以达到解决证据冲突的目的。最后,以网络安全态势感知环境为背景进行数值算例,证明了所提方法的可行性。     

基于模糊逻辑和组合证据理论的信息融合技术在网络管理中的应用

研究基于模糊逻辑和组合证据理论的综合信息融合技术在网络管理中的应用．研究了用于网络管理的来源于多信息源的关联规则的融合方法和推理机制，以及故障与故障原因的模糊关系和模糊规则的融合方法及推理机制；在故障定位方面，采用组合证据理论对网络专家、规则推理和模糊推理所给出的故障原因进行融合得出综合的诊断结果。     

基于D-S证据理论的传感器网络数据融合算法

在传感器网络中，多个传感器对于同一目标的识别结果经常会发生冲突，本文采用基于Dempster—Sharer证据推理理论的数据融合方法来解决这一问题。然而，采用D—S证据组合公式计算融合结果，计算量过于巨大，对处理能力有限的感知结点来说负担过重，此外，计算所造成的延时也将严重影响系统的实时性和同步性．本文提出了一个基于矩阵分析的快速融合算法，该算法采用了D—S证据理论的思想，计算得到的融合结果与D—S证据组合公式计算得到的融合结果相同．本文用数学归纳法证明了这一结论，经过模拟实验验证，和直接采用D—S证据组合公式相比，该算法的计算量和所需的计算时间明显减少．     

A robust continuous model for evidential reasoning

A 2-D model for evidential reasoning is proposed, in which the belief function of evidence is represented as a belief density function which can be in a continuous or discrete form. A vector form of mutual dependency relationship of the evidence is considered and a dependency propagation theorem is proved. This robust method can resolve the conflicts resulting from either the mutual dependency among evidences or the structural dependency in an inference network due to the evidence combination order. Belief conjunction, belief combination, belief propagation procedures, and AND/OR operations of an inference network based on the proposed 2-D model are all presented, followed by some examples demonstrating the advantages of this method over the conventional methods.     

基于D-S证据理论的网络安全风险评估模型

针对网络信息系统的安全风险评估问题,提出了基于D-S证据理论的风险评估模型。证据理论是一种处理不确定性的推理方法。首先用"折扣率"对Dempster合成法则进行了改进,然后使用改进后的Dempser合成法则对网络中存在的各种风险因素进行合成,减少了风险因素中的不确定性,并以实例验证了该模型在网络安全风险评估中的应用,仿真结果证明了该算法的正确性。最后,通过与模糊综合评判法进行比较验证了证据理论具有更高的准确性。     

基于C-BRB模型的发动机运行状态监测方法

针对发动机运行状态监测过程中发动机内部多个因素之间相关性与建模方法可解释性问题,提出数据驱动下C-BRB方法.该方法首先通过样本数据计算发动机内部多个因素之间的Kendall秩相关系数,并确定具体Copula模型及参数λ,实现对多个因素之间相关性的测量;然后使用置信规则库(BRB)对发动机内部多个因素建模,在BRB推理...