In this paper,an alternating identification algorithm with a smoothing factor for nonlinear systems based on data is proposed for the problem of index forecasting for complex industrial processes.The alternating identification algorithm expands the input and output model of the system into a linear model and a higher-order nonlinear model near the operating point.Then,the parameters of the linear model and nonlinear model are updated alternately.The least squares identification method is used for the linear model,and the long-short memory network is used for the higher-order nonlinear model.The innovation of the proposed method is that for the problem that the noise in the actual system is easy to cause the oscillation of the identification parameters of the linear part,the smoothing factor is introduced to suppress the oscillation.In the nonlinear part,the compression factor is introduced to adjust the weight of the nonlinear part in the identification process,which improves the accuracy of the forecast.The performance of the proposed algorithm was verified by numerical simulation and compared with other methods.The results show that the proposed algorithm can effectively suppress parameter oscillation in the identification process and achieve better identification accuracy.
关键词
智能控制/复杂工业过程/运行指标预报/平滑交替辨识
Key words
intelligent control/complex industrial process/operational index prediction/smooth alternate identification
维普
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