基于团树传播的证据网络推理算法

将团树传播算法应用于证据网络中,解决复杂的多连通知识网络结构下的信度推理问题.将复杂多连通网络构造成一棵团树,并将联合信度作为团节点的参数实现复杂多连通网络结构上的证据网络信度推理.在进行联合信度函数信息融合的过程中,通过引入两种新的交并运算实现对DSmT组合规则的改进,减少不确定性.最后通过实例验证了所提出方法的可行性.

Reliability inference method for evidential network based on the clique tree propagation algorithm

For solving the reliability inference problem in the multi-connected knowledge network model, a clique tree propagation algorithm is innovatively applied to the evidential network.Firstly, the multi-connected knowledge network model is clustered as a clique tree, and the joint belief function is regarded as the main parameter of the cluster nodes, therefore, the information each node is obtained, which facilitates the possibility of reliability inference within the multi-connected knowledge network model. In the process of evident fusion of the joint belief function, two new union and intersection methods are introduced to improve the existing DSmT theory, which helps to eliminate the influence of conflicting evident information on other evident variables. Finally, an example is given to illustrate the feasibility of the proposed method.