NOISY OBSERVATION BASED STABILIZATION AND OPTIMIZATION FOR UNKNOWN SYSTEMS

The paper addresses optimization of a performance function which either is optimized via stabilizing and controlling the underlying unknown system or is directly optimized on the basis of its noise-corrupted observations. For the first case the unknown system is identified and then the indirect adaptive control approach is applied to optimize the performance function. For the second case the stochastic approximation method is used to optimize the objective function, and it appears that a number of problems arising from applications may be reduced to the one solvable by this approach. The paper demonstrates some basic results in the area, but with no intention to give a complete survey.     

IDENTIFICATION FOR WIENER SYSTEMS WITH INTERNAL NOISE*

This paper considers identification of Wiener systems for which the internal variables and output are corrupted by noises. When the internal noise is a sequence of independent and identically distributed （lid） Gaussian random variables, by the Weierstrass transformation （WT） the system under consideration turns to be a Wiener system without internal noise. The nonlinear part of the latter is nothing else than the WT of the nonlinear function of the original system, while the linear subsystem is the same for both systems before and after WT. Under reasonable conditions, the recursive identification algorithms are proposed for the transformed Wiener system, and strong consistency for the estimates is established. By using the inverse WT the nonparametric estimates for the nonlinearity of the original system are derived, and they are strongly consistent if the nonlinearity in the original system is a polynomial, Similar results also hold in the case where the internal noise is non-Gaussian. Simulation results are fully consistent with the theoretical analysis.     

POPOV METHOD FOR ROBUST ANALYSIS AND ROBUST SYNTHESIS OF MIMO LINEAR SYSTEMS WITH MRAMETRIC UNCERTAINTIES

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ROBUST STABILITY WITH GUARANTEEING COST FOR DISCRETE TIME-DELAY SYSTEMS WITH NONLINEAR PERTURBATION

The problems of robust stability and robust stability with a guaranteeing cost for discrete time-delay systems with nonlinear perturbation are discussed. A sufficient criterion for robust stability is established in an LMI framework and a linear convex optimization problem with LMI constraints for computing maximal perturbation bound is proposed. Meanwhile, a sufficient criterion for robust stability with a guaranteeing cost for such systems is obtained, and an optimal procedure for decreasing the value of guaranteeing cost is put forward. Two examples are used to illustrate the efficiency of the results.     

ROBUST KALMAN FILTERING FOR SYSTEMS UNDER NORM BOUNDED UNCERTAINTIES IN ALL SYSTEM MATRICES AND ERROR COVARIANCE CONSTRAINTS

This paper concerns robust Kalman filtering for systems under norm bounded uncertainties in all the system matrices and error covariance constraints. Sufficient conditions are given for the existence of such filters in terms of Riccati equations. The solutions to the conditions can be used to design the filters. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed design procedure.     

多计算机并行系统的互连与通信结构

多计算机系统的关键设计之一是互连网络。本文分析和介绍了用于多计算机并行系统的各种典型的互连结构,从拓扑角度分析了各种互连结构的通信性能和路径控制算法。重点讨论了链路型和总线型互连网络,并分析了通信的局部性对性能的影响。     

A BI-LEVEL FORMULATION AND QUASI-NEWTON ALGORITHM FOR STOCHASTIC EQUILIBRIUM NETWORK DESIGN PROBLEM WITH ELASTIC DEMAND

1 IntroductionThe network design problem (NDP) is to select link improvements or add new links to anexisting network suCh that social welfare is maximized while according for the route choicebehavior of network users and/or changes in demand. Two sets of decision-makers with differedsobjectives are involved in the NDP. The network users individually select their routes suchthat their individual travel costs are minimized, while the planners aim to make the bestnetwork improvements for reduc…     

基于随机规划的制造/再制造物流网络优化设计

针对含有连续型随机参数的制造/再制造物流网络优化设计问题,在Monte Carlo模拟抽样基础上,建立了样本数量决定解算效率的两阶段随机规划模型,给出了模型求解的混合遗传算法,结合样本均值近似方法阐述了获取理想目标值及其可行解的最优值上下界逼近技术,明确了基于两阶段随机规划的物流网络优化设计步骤,举例说明了模型及其算法在设计决策中的应用。     

A NEW ALGORITHM FOR PURX O-1 LINEAR PROGRAMS WITH INEQUALITY CONSTRAINTS

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