烧结矿FeO神经网络实时预测模型的工程实现

Sinter Fe O Neural Network Forecasting Model Works to Achieve Real-time

基于径向基函数神经网络的烧结矿Fe O实时预测模型是目前为止较好的预测模型,前人对它的研究主要在理论分析及计算机仿真研究阶段,前人的研究成果指出了其良好的应用前景;为将其应用于钢铁生产过程的实时控制,本文对神经网络模型的工程实施进行了研究;不同的应用场合,实现神经网络模型的软件及硬件平台也各不相同。本文详细阐述了基于Neurosystems软件平台以及S7-400 PLC硬件平台的基于径向基函数神经网络的烧结矿Fe O实时预测模型的实现技术。

Sinter FeO based real-time forecasting model based on radial basis function neural network is a far better forecasting models, previous studies of its main theoretical analysis and computer simulation in the research stage, the results of previous research points out the good application prospects; to apply it to real-time control of steel production process, this paper neural network model implementation of the project has been studied; different applications, implementing software and hardware platform for the neural network model is also different. This paper describes the implementation technology sinter FeO based on RBF neural network forecasting model Neurosystems real-time software platforms and S7-400 PLC hardware platform.