共查询到20条相似文献,搜索用时 222 毫秒
1.
2.
3.
4.
5.
6.
基于ARMAX模型的广义预测控制 总被引:5,自引:0,他引:5
本文指出Clarke等人的广义预测控制适用于干扰噪声功率趋向无穷的CARIMA模型。本文给出了一种基于ARMAX模型的广义预测控制器,这种广义预测控制具有较强的稳健性(Robustness)。文末给出了数字仿真例子。 相似文献
7.
本文在广义预测控制(G.P.C)的基础上,提出一种非参数模型的前馈广义预测控制算法,并把这种算法应用到高纯度精馏塔双组分控制中去.计算机仿真验证了它的可行性,解耦效果是较好的。 相似文献
8.
广义预测控制:理论、算法与应用* 总被引:24,自引:1,他引:24
本文简要介绍了广义预测控制基本方法及其近年来提出的改进广义预测控制算法,并对非互性系统广义预测控制、多变量系统广义预测控制、广义预测控制的稳定性与鲁棒性分析作了进一步讨论,最后,概述了广义预测控制在工业中的应用和广义预测控制的进一步研究方向。 相似文献
9.
泛Kriging和广义线性回归模型是空间对象属性值预测最常用的方法,两种模型各有侧重.本文结合这两种模型的优点,并针对实际问题将两种模型合并,形成新的空间广义线性回归模型,并通过混合模型进行参数估计.实验表明,采用空间广义线性回归预测模型能获得更高的预测精度. 相似文献
10.
《计算机应用与软件》2013,(4)
利用2005年至2011年中国内地法定报告的乙肝发病数资料分别建立广义回归神经网络模型以及传统的BP神经网络模型,探讨广义回归神经网络在乙肝发病预测中的实用价值。结果显示,广义回归神经网络拟合及预测结果的平均绝对误差,平均相对误差以及均方误差均小于BP神经网络。该结果提示,广义回归神经网络在乙肝发病数预测中具有较好的应用价值。 相似文献
11.
比例–积分控制加广义预测控制算法及其应用 总被引:1,自引:0,他引:1
针对比例–积分(proportional-integral, PI)控制因不能预测未来输出而提前改变控制量使其用于光电稳定伺服系统时往往响应剧烈的问题,研究了光电稳定伺服系统的广义预测控制(generalized predictive control, GPC).首先通过证明受控自回归积分滑动平均(controlled auto-regressive integral moving-average, CARIMA)模型的直接递推预测与Diophantine方程预测等价,提出了预测较快的模型等价预测GPC算法,其预测复杂度比原GPC降低了一个阶次.其次通过对PI和GPC的特点进行分析,综合考虑两者的优缺点,提出了一种新型的基于PI增量和GPC增量加权的比例积分控制加广义预测控制(proportional-integral control plus generalized predictive control, PI+GPC)算法,实现了基于历史、当前和未来偏差计算控制量,并给出了算法设计流程和参数选取规则.最后通过仿真并在某光电稳定伺服平台上验证后得出, PI+GPC和PI相比稳定精度有所提高,且平稳性和快速性大为改善. 相似文献
12.
13.
14.
15.
16.
The original ARMarkov identification method explicitly determines the first μ Markov parameters from plant input–output data and approximates the slower dynamics of the process by an ARX model structure. In this paper, the method is extended to include a disturbance model and an ARIMAX structure is used to approximate the slower dynamics. This extended ARMarkov model is then used to formulate a predictive controller. As the number of Markov parameters in the model varies from one to P (prediction horizon)+1, the controller changes from generalized predictive control (GPC) to dynamic matrix control (DMC). The advantages of the proposed ARM-MPC are the consistency of the Markov parameters estimated by the ARMarkov method, independent tuning of the controller for servo and regulatory responses and the ability to combine the characteristics of GPC and DMC. The theoretical results are illustrated through simulation examples. 相似文献
17.
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controUed plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm. 相似文献
18.
针对股票交易过程中价格转折点的预测问题,提出了一种基于分段线性表示(PLR)与高斯过程分类(GPC)相结合的股票价格转折点预测算法PLR-GPC。该算法通过PLR提取股票历史价格序列的转折点,对转折点进行分类标记,建立基于GPC的股票价格转折点预测模型,以上述股票历史价格序列对模型进行训练,最终由预测模型对股票价格转折点进行预测,并对预测结果进行概率解释。将PLR-GPC与基于BP神经网络(BPN)的PLR-BPN算法、基于加权支持向量机支持向量机(WSVM)的PLR-WSVM算法进行实验对比:PLR-GPC在预测准确率上高于PLR-BPN与PLR-WSVM;在投资收益率上高于PLR-BPN,与PLR-WSVM持平。实验结果表明PLR-GPC在股票价格转折点的预测上是有效的,并且可以应用在实际股票投资交易中。 相似文献
19.
On the practice of artificial intelligence based predictive control scheme: a case study 总被引:2,自引:2,他引:0
This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many
complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang
(TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized
predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated
model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated
multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier,
while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme,
an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the
proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained
using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results
of the proposed control scheme are desirably verified. 相似文献
20.