带自相关约束的NARX动态软测量模型

非线性带外输入自回归模型(NARX)在进行预测估计时依赖于主导变量的实时测量,因此在实际工业过程中存在一定的实施难度.针对该问题,利用神经网络构造一种新型NARX动态软测量模型,当工业过程无法及时提供上时刻主导变量测量值时,能通过多步预测方法来确保主导变量的实时预测,通过设计模型结构来降低预测序列的自相关性,从而抑制由多步估计造成的累积误差,以适当降低单步预测精度为代价,使模型在主导变量检测时间长、采样周期长、测量存在噪声的工业场合下得到更好的预测效果.通过数学分析和脱丁烷塔数据仿真实验验证了所构建模型的有效性.

Autocorrelation constrained NARX dynamic soft sensing model

Due to the excessive dependence on the real time measurement of dominant variables during prediction, it is difficult to apply the nonlinear autoregressive with exogenous input(NARX) model in real industrial processes.In order to solve the difficulty, a new NARX model structure is built by combining two network models.When the industrial environment cannot provide the history information of dominant variables in real time, the real-time prediction of dominant variables can be guaranteed by using the multi-step prediction method.By reducing the autocorrelation of prediction sequence, the cumulative error caused by multi-step estimation is suppressed. At the cost of reducing the accuracy of single-step prediction properly, the model can obtain better prediction effect in the much rough industrial environment.Simulation results verify the effectiveness of the proposed model.