计算机应用与软件2018，Vol.35Issue(3)：151-156,6.DOI:10.3969/j.issn.1000-386x.2018.03.029
基于BP神经网络和改进D-S证据理论的目标识别方法
TARGET RECOGNITION METHOD BASED ON BP NEURAL NETWORKS AND IMPROVED D-S EVIDENCE THEORY
摘要
Abstract
When using classical D-S evidence theory, the basic probability assignment(BPA)of key parameters is often obtained by subjective experience,leading to the problem of low credibility of decision-making.A method to obtain the basic probability assignment by constructing BP neural network was proposed.The method utilized the powerful self-learning and non-linear mapping ability of BP neural network to normalize the output value to get the basic probability assignment.At the same time,in order to solve the paradoxical problem of synthetic evidence with high conflict degree, a new fusion method based on evidence trust factor was proposed.According to the trust factor of the evidence, giving the corresponding weight,the expected evidence was obtained after weighted average.Then fused expected evidence by D-S fusion formula.The experimental results showed that the proposed method could eliminate the influence of high conflict evidence on the synthesis results,and had a higher accuracy of target recognition.关键词
D-S证据理论/BP神经网络/信息融合/目标识别Key words
D-S evidence theory/BP neural networks/Information fusion/Target recognition分类
信息技术与安全科学引用本文复制引用
张志,杨清海..基于BP神经网络和改进D-S证据理论的目标识别方法[J].计算机应用与软件,2018,35(3):151-156,6.基金项目
国家自然科学基金项目(61471287). （61471287）
