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通过对BP网络的输入层节点增加了自反馈,使BP网络对历史数据具有了记忆功能,改进后的局部自反馈BP神经网络具有动态映射和处理历史数据的能力.使用该局部自反馈BP神经网络实现了多信息的融合算法;通过对工矿企业现场诊断技术、信息融合方法和物联网应用分析,设计了一个基于物联网的工矿企业现场诊断与管理系统,并将局部自反馈BP神经网络信息融合算法应用到基于物联网现场诊断系统的云计算层,为设备生产厂家和工矿企业应用物联网进行现场诊断与管理提供了一个解决方案.
By adding self-feedback to the input layer node of BP network, BP network has memory function for historical data, and the improved local self-feedback BP neural network has the ability of dynamic mapping and processing of historical data.Using this local self-feedback BP neural network The network realizes the multi-information fusion algorithm. Based on the on-site diagnosis technology, information fusion method and IOT application analysis of industrial and mining enterprises, an on-site diagnosis and management system of industrial and mining enterprises based on Internet of Things is designed. Local self-feedback BP neural network information The fusion algorithm is applied to cloud computing layer based on IoT field diagnosis system, which provides a solution for on-site diagnosis and management of IoT by equipment manufacturers and industrial and mining enterprises.