首页> 外文会议>2012 Third International Conference on Digital Manufacturing and Automation >An Improve Information Fusion Algorithm Based on BP Neural Network and D-S Evidence Theory
【24h】

An Improve Information Fusion Algorithm Based on BP Neural Network and D-S Evidence Theory

机译:基于BP神经网络和D-S证据理论的改进信息融合算法。

获取原文
获取原文并翻译 | 示例

摘要

BP neural network and DS evidence theory have gotten a wide range of applications in the field of information fusion. According to the BP neural network have low recognition rate and poor network stability, what is more, it is difficult to get D-S evidence theory of basic probability distribution function, This paper design a kind of improved algorithm, which combined group neural network and D-S evidence theory. The improved algorithm make full use of the advantages. The simulation results show that this algorithm have a better effect both in recognition rate and anti-noise capacity.
机译:BP神经网络和DS证据理论已经在信息融合领域得到了广泛的应用。鉴于BP神经网络识别率低,网络稳定性差,而且难以获得基本概率分布函数的DS证据理论,本文设计了一种改进的算法,将群体神经网络和DS证据相结合。理论。改进算法充分利用了这些优点。仿真结果表明,该算法在识别率和抗噪能力上都有较好的效果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号