采用粗BP神经网络和D-S证据理论的目标识别

为克服传统的目标识别方法的不足,提高目标识别的实时性和准确性,提出将粗BP神经网络与D-S证据理论相结合的识别模型.在多传感器数据融合中利用粗集理论对大量的传感器数据进行处理,对输入信息进行约简,剔除冗余信息,简化了生成规则和BP神经网络模型结构,提高了网络训练速度和运行速度.以BP神经网络输出作为证据,后端对不同传感器的证据用D-S证据理论进行融合,得到待识别目标的识别概率.实验表明该模型减少了识别的主观因素,简化了BP神经网络结构,提高了运算速度和识别效果.该混合模型有比较好的应用前景.

Target recognition method based on combination of rough BP neural network and D-S evidence theory

In order to overcome the shortages of traditional target recognition method and improve the real-time performance and precision of target recognition,a target recognition model is put forward which combines the rough BP neural network with D-S evidence theory.Rough BP set theory is used to deal with the great deal of data from sensors in multi-sensor data fusion,predigest the input information and eliminate the redundant information,thus can simplify the BP neural network model structure and improve the net...