基于稳定Hammerstein模型的在线软测量建模方法及应用
On-line soft sensor based on stable Hammerstein model and its applications
查看参考文献21篇
文摘
|
针对复杂工业过程中由于存在未建模动态和不确定干扰,导致关键变量的软测量精度下降的问题,提出了一种基于稳定Hammerstein模型(H模型)的在线软测量建模方法。H模型的非线性增益采用带有时变稳定学习算法的小波神经网络模型,线性系统部分采用基于递推最小二乘的ARX模型,基于输入到状态稳定性理论证明了H模型辨识误差的有界性。其中小波神经网络具有表征强非线性的特性,稳定学习算法可抑制未建模动态和不确定干扰的影响,改善了模型的预测精度和自适应能力。以典型非线性系统和实际污水处理过程为例进行了仿真研究,结果表明,基于稳定H模型的软测量方法具有较高的在线软测量精度。 |
其他语种文摘
|
Aiming at the problem that the soft sensing precision of key variables deteriorates when unmodeled dynamics and uncertain disturbances exist in the complex industrial process, an on-line soft sensor based on stable Hammerstein model (H model) was presented. H model was composed of wavelet neural network with time-varying stable learning algorithm as nonlinear gain and ARX model with RLS (recursive least square) algorithm as linear part. The boundedness of identification error for H model was proved according to the Input-to-State Stability theory. Wavelet neural network could represent strong nonlinearity of the process, and the stable learning algorithm could restrain the influences of unmodeled dynamics and uncertain disturbances and improve prediction precision and self-adaptability. Simulations based on a nonlinear system and the wastewater treatment process showed that the soft sensing method presented in this paper possessed high prediction precision. |
来源
|
化工学报
,2015,66(4):1380-1387 【核心库】
|
DOI
|
10.11949/j.issn.0438-1157.20141210
|
关键词
|
Hammerstein模型
;
在线建模
;
软测量
;
预测
;
稳定学习
;
污水处理过程
;
稳定性
|
地址
|
1.
辽宁石油化工大学信息与控制工程学院, 中国科学院网络化控制系统重点实验室, 辽宁, 抚顺, 113001
2.
中国科学院沈阳自动化研究所信息服务与智能控制技术研究室, 中国科学院网络化控制系统重点实验室, 辽宁, 沈阳, 110016
|
语种
|
中文 |
文献类型
|
研究性论文 |
ISSN
|
0438-1157 |
学科
|
自动化技术、计算机技术 |
基金
|
国家自然科学基金项目
;
中国博士后科学基金
;
中国科学院网络化控制系统重点实验室自主课题
|
文献收藏号
|
CSCD:5393779
|
参考文献 共
21
共2页
|
1.
柴天佑. 复杂工业过程运行优化与反馈控制.
自动化学报,2013,39(11):1744-1757
|
被引
52
次
|
|
|
|
2.
曹鹏飞. 化工过程软测量建模方法研究进展.
化工学报,2013,64(3):788-800
|
被引
53
次
|
|
|
|
3.
Yu W. Discrete-time neuro identification without robust modification.
IEE Proceedings -Control Theory and Applications,2003,150(3):311-316
|
被引
2
次
|
|
|
|
4.
Zhang N M. An online gradient method with momentum for two-layer feedforward neural networks.
Applied Mathematics and Computation,2009,212(2):488-498
|
被引
1
次
|
|
|
|
5.
Chen Y J. An effective learning of neural network by using RFBP learning algorithm.
Information Sciences,2004,167(1):77-86
|
被引
3
次
|
|
|
|
6.
Narendra K S. An iterative method for the identification of nonlinear systems using a Hammerstein model.
IEEE Transactions on Automatic Control,1966,11(3):546-550
|
被引
31
次
|
|
|
|
7.
Lakshminarayanan S. Modeling and control of multivariable processes: dynamic PLS approach.
AIChE Journal,1997,43(9):2307-2322
|
被引
15
次
|
|
|
|
8.
Wlodzimierz G. Stochastic approximation in nonparametric identification of Hammerstein systems.
IEEE Transactions on Automatic Control,2002,47(11):1800-1811
|
被引
6
次
|
|
|
|
9.
Zhao W X. Parametric identification of Hammerstein systems with consistency results using stochastic inputs.
IEEE Transactions on Automatic Control,2010,55(2):474-480
|
被引
7
次
|
|
|
|
10.
贾立. Hammerstein模型辨识的回顾及展望.
控制理论与应用,2014,31(1):1-10
|
被引
14
次
|
|
|
|
11.
Jia L. Special input signals based neurofuzzy Hammerstein-Wiener model and its application.
International Journal of System Control and Information Processing,2012,1(2):199-218
|
被引
2
次
|
|
|
|
12.
Hunt K J. Neural networks for control systems-a survey.
Automatica,1992,28(6):1083-1112
|
被引
80
次
|
|
|
|
13.
陈森发.
复杂系统建模理论与方法,2005
|
被引
15
次
|
|
|
|
14.
Yu W. Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms.
Information Sciences,2004,158(1):131-147
|
被引
6
次
|
|
|
|
15.
Sontag E D. On characterizations of the input-to-state stability property.
Systems and Control Letters,1995,24(5):351-359
|
被引
49
次
|
|
|
|
16.
冯培悌.
系统辨识,1999
|
被引
36
次
|
|
|
|
17.
王双剑. Hammerstein模型的改进新型神经动力学辨识方法及其在混合建模中的应用.
信息与控制,2012,41(3):384-390
|
被引
4
次
|
|
|
|
18.
Kreinovich V S O. Wavelet neural networks are asymptotically optimal approximators for function of one variable.
Proceeding of IEEE ICNN,1994:299-304
|
被引
1
次
|
|
|
|
19.
叶凌箭. 基于被控变量在线建模的化工过程实时优化方法.
化工学报,2013,64(8):2918-2923
|
被引
1
次
|
|
|
|
20.
Sontag E D. Input to state stability: basic concepts and results.
Lecture Notes in Mathematics,2008,1932:163-200
|
被引
3
次
|
|
|
|
|