摘要
针对复杂工业过程的指标预报问题,本文提出一种基于数据的非线性系统平滑交替辨识算法.交替辨识算法将系统的输入输出模型在工作点附近展开为线性模型和高阶非线性模型,然后交替更新线性模型参数和非线性模型参数,其中对于线性模型采用最小二乘辨识方法,对于高阶非线性模型采用长短期记忆网络进行建模.所提方法的创新之处在于,对于实际系统中的噪声易导致线性部分辨识参数震荡的问题,引入平滑因子来抑制震荡,提高预测模型的稳定性能;在非线性部分则引入压缩因子来调节在辨识过程中非线性部分的权重,总体上提高了预报的准确性.通过数值仿真验证了所提算法的性能,并与其他方法进行了对比实验,结果表明所提算法能够有效抑制辨识过程中的参数震荡,并且取得更好的辨识精度.
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.
作者
谌卓玲
卢绍文
张亚军
潘庆玉
CHEN Zhuo-ling;LU Shao-wen;ZHANG Ya-jun;PAN Qing-yu(State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang Liaoning 110819,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2024年第9期1539-1547,共9页
Control Theory & Applications
基金
国家自然科学基金重点项目(61833004)
国家重点研发计划项目(2020YFB1713602)
国家自然科学基金项目(61991402,61890923,61973202,61873052)资助.
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
intelligent control
complex industrial process
operational index prediction
smooth alternate identification
作者简介
谌卓玲,硕士研究生,目前研究方向为复杂工业过程非线性控制与建模,E-mail:springczl0915@163.com;通信作者:卢绍文,教授,目前研究方向为工业过程建模与仿真、多尺度随机建模、数字孪生与可视化技术,E-mail:lusw@mail.neu.edu.cn;张亚军,副教授,目前研究方向为非线性模糊自适应控制理论、广义预测控制、多模型切换控制、智能解耦控制、智能控制系统的大数据建模、工业过程大数据建模及应用,E-mail:yajunzhang@mail.neu.edu.cn;潘庆玉,硕士研究生,目前研究方向为工业过程随机建模与多尺度仿真方法,E-mail:1207816946@qq.com.
