An improved method to D-S evidence theory based on the reward and punishment factors

Zhengxiong Ji; Jianyan Tian; Zhihuan Zhang

doi:10.1117/12.2646957

7 September 2022 An improved method to D-S evidence theory based on the reward and punishment factors

Zhengxiong Ji, Jianyan Tian, Zhihuan Zhang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290J (2022) https://doi.org/10.1117/12.2646957
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

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.

Citation Download Citation

Zhengxiong Ji, Jianyan Tian, and Zhihuan Zhang "An improved method to D-S evidence theory based on the reward and punishment factors", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290J (7 September 2022); https://doi.org/10.1117/12.2646957

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KEYWORDS

Americium

Computer simulations

Sensors

Probability theory

Reliability

Electrical engineering

Intelligent sensors

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