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71.
An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor. 相似文献
72.
This paper1 considers a single product and a single stocking location production/inventory control problem given a non-stationary stochastic
demand. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number
of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service
level. We extend a known CP approach for this problem using three cost-based filtering methods. Our approach can solve to
optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all.
This work was supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications
Value-Chain-Driven Research (CTVR) and Grant No. 00/PI.1/C075.
1This paper is an extended version of [19]. 相似文献
73.
Xi-Ren 《Annual Reviews in Control》2009,33(1):11-24
We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic systems. We show that this sensitivity-based view provides a unified framework for many different disciplines in this area, including perturbation analysis, Markov decision processes, reinforcement learning, identification and adaptive control, and singular stochastic control; and that this unified framework applies to both the discrete event dynamic systems and continuous-time continuous-state systems. Many results in these disciplines can be simply derived and intuitively explained by using two performance sensitivity formulas. In addition, we show that this sensitivity-based view leads to new results and opens up new directions for future research. For example, the n th bias optimality of Markov processes has been established and the event-based optimization may be developed; this approach has computational and other advantages over the state-based approaches. 相似文献
74.
75.
Hans M. Amman Author Vitae David A. Kendrick Author Vitae Author Vitae 《Automatica》2008,44(6):1504-1510
Currently, there is a renewed interest in the use of optimal experimentation (adaptive control) in economics. Example are found in
[Amman and Kendrick, 1999],
[Amman and Kendrick, 2003],
[Cosimano, in?press],
[Cosimano and Gapen, 2005b],
[Cosimano and Gapen, 2005a],
[Cosimano and Gapen, 2006],
[Tesfaselassie et?al., 2007],
[Tucci, 1997],
[Wieland, 2000a] and [Wieland, 2000b]. In this paper we present the Beck & Wieland model [Beck, G., & Wieland, V. (2002). Learning and control in a changing economic environment. Journal of Economic Dynamics and Control, 26, 1359-1378] and the methodology to solve this model with time-varying parameters using the various control methods described in
[Kendrick, 1981] and [Kendrick, 2002]. Furthermore, we also provide numerical results using the DualPC software [Amman, H. M., & Kendrick, D. A. (1999). The DualI/DualPC software for optimal control models: User’s guide. Working paper, Austin, TX 78712, USA: Center for Applied Research in Economics, University of Texas] and show first evidence that optimal experimentation or Dual Control may produce better results than Expected Optimal Feedback. 相似文献
76.
Yuewu Dong Author Vitae Author Vitae 《Automatica》2008,44(4):990-995
In this paper, we address the problem of hybrid control for a class of stochastic non-linear Markovian switching systems. First, a hybrid controller is introduced for the systems. Then under some appropriate assumptions, the stabilization condition for the systems under pure impulsive control is given. Further under impulsive control, the output feedback stabilization problem of the systems is discussed and linear output feedback controllers are designed. Finally a numerical example is provided to illustrate the effectiveness of the proposed methods. 相似文献
77.
78.
79.
现有的节点边际电价机制中,由于传统发电商具有市场操控力,当储能独立参与市场出清时,各发电商采取策略性报价,打压并挤占储能电站的市场份额,阻碍了储能电站参与市场出清,间接导致市场出清总成本增大。为此,文中提出一种包含传统机组以及储能电站参与的市场竞争机制。首先,分析现有市场结算机制的弊端以及阻碍储能参与市场出清的原因;其次,建立含储能参与的市场出清模型,采用样本均值近似求解二阶段随机规划模型;接着,基于VCG结算机制,提出适应储能参与的日前市场价值分配机制;最后,提出解决激励相容而造成的系统收支不平衡问题的策略。文中采用修改后的IEEE30节点为例,证明该机制满足激励相容、收支平衡以及削弱传统发电商的市场操控力等性质,同时储能的参与将会减小系统出清总成本,降低市场价格剧烈波动的风险。 相似文献
80.
The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle. 相似文献