An improved method to D-S evidence theory based on the reward and punishment factors
Zhengxiong Ji, Jianyan Tian, Zhihuan Zhang · Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) · 2022
Aiming at the problem that the classical evidence combination formula will fail in case of serious conflict between evidences, an improved method of Dempster-Shafer (D-S) evidence theory based on reward and punishment factors is proposed. Firstly, a conflict allocation formula based on the basic probability assignments is constructed, and then a calculation method of reward and punishment factors based on credibility is proposed. The reward and punishment factors are substituted into the conflict allocation formula to correct the proportion of conflict allocation. Finally, the modified conflict allocation formula is used for evidence fusion. Simulation results show that the proposed algorithm can effectively solve the conflict between pieces of evidence and has better convergence and stability compared to the classical combination formula and other improved methods when multiple pieces of evidence are fused. In the process of practical application, the performance of the algorithm can be changed by adjusting the size of specific parameters, which improves the flexibility of the application of the algorithm.