中文说明：神经网络的并行结构和并行处理机制，使得信息处理速度快，能够满足信息的实时处理要求。将神经网络用于多传感器数据融合技术，首先要根据系统的要求以及传感器的特点选择合适的神经网络模型（常见的神经网络模型有BP网络、Hop-field网络、Elman网络、RBF网络等），然后再根据已有的多传感器数据和系统的融合知识采用一定的学习方法，对建立的神经网络系统进行离线学习，确定网络的联接权值和联接结构，最后把得到的网络用于实际的数据融合当中。
English Description：
The parallel structure and parallel processing mechanism of neural network make the information processing speed fast and meet the requirements of real-time information processing. In order to apply neural network to multi-sensor data fusion technology, we must first select the appropriate neural network model according to the requirements of the system and the characteristics of the sensor (the common neural network models are BP network, hop field network, Elman network, RBF network, etc.), and then use certain learning methods according to the existing multi-sensor data and system fusion knowledge to build the neural network model The network system carries out off-line learning, determines the connection weight and connection structure of the network, and finally applies the obtained network to the actual data fusion.