摘要
研究了粗集和神经网络方法在信息融合目标识别中的应用,提出将神经网络学习机制引入到粗集系统,同时通过粗集的条件和决策属性构造神经网络结构,并针对三种不同谱段下的三种不同目标图像进行了实验,试验表明,粗集神经网络相结合的识别算法的识别率要明显高于单独使用一种融合算法的识别率,训练时间也大大缩短。
The application of rough sets combined with neural network method in data fusion target recognition is developed in this paper. The learning mechanism has been introduced into the rough sets system and the neural network is built up according to the attributes of conditions and the decision rules of the rough sets. Finally, the experiments on three different target images in three different spectrums show that the combination of rough sets and neural network lead to a much higher recognition rate than one dam fusion algorithm alone, and the training time is reduced to large scale.
出处
《实验科学与技术》
2007年第2期40-41,57,共3页
Experiment Science and Technology
关键词
粗集
神经网络
信息融合
目标识别
rough sets
neural network
data fusion
target recognition
