基于因素分析的复合神经网络及其在软测量中的应用

Multiple Neural Networks Based on Factor Analysis and its Application in Soft Sensor

  • 摘要: 针对工业生产中,众多因素对生产影响程度不同的特点,提出了一种基于因素分析的复合神经网络(FA-MNN)模型.介绍了FA-MNN模型的结构,改进了神经网络的学习算法,并将其应用于氧化铝高压溶出过程中苛性比值及溶出率的软测量,利用现场实际运行数据进行仿真,结果表明FA-MNN模型能有效实现苛性比值及溶出率的在线检测.

     

    Abstract: Considering the fact that a large number of factors influence the industry process and their influence varies, a multiple neural networks (FA-MNN) model based on factor analysis is proposed. The structure of FA-MNN is introduced and its learning algorithm is improved. The FA-MNN model is applied in the process of high pressure digestion of alumina. Simulation results show that the FA-MNN model can sense the ratio of soda to aluminate and leaching rate online effectively.

     

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