连续时间遗传算法模型及其强收敛性分析

本文在隐马尔可夫链的框架下利用隐马尔可夫链{Xt:t∈[0,∞)}的观测链的概率分布提出一个连续化遗传算法模型，并给出其一个强收敛结果，讨论了其离散骨架的性质。     

Cluster algorithms for anisotropic quantum spin models

We present cluster Monte Carlo algorithms for theXYZ quantum spin models. In the special case ofS=1/2, the new algorithm can be viewed as a cluster algorithm for the 8-vertex model. As an example, we study theS=1/2XY model in two dimensions with a representation in which the quantization axis lies in the easy plane. We find that the numerical autocorrelation time for the cluster algorithm remains of the order of unity and does not show any significant dependence on the temperature, the system size, or the Trotter number. On the other hand, the autocorrelation time for the conventional algorithm strongly depends on these parameters and can be very large. The use of improved estimators for thermodynamic averages further enhances the efficiency of the new algorithms.     

Two machine open shop scheduling problem to minimize an arbitrary machine usage regular penalty function

The paper present a linear-time algorithm for solving the two machine open shop scheduling problem to minimize an arbitrary regular penalty function depending on the lengths of periods during which the machines are used. Both the preemptive and the nonpreemptive cases of the problem are considered.     

Open shop problems with unit time operations

We show that them-machine open shop problem in which all operations have unit processing times can be polynomially transformed to a special preemptive scheduling problem onm identical parallel machines. Many results published recently as well as some new results are derived by using this transformation. The new results include solutions of open problems mentioned in a recent paper by Kubiak et al. p]A similar relationship is derived between no-wait open shop problems with unit time operations andm-machine problems with jobs having unit processing times.This work was supported by Deutsche Forschungsgemeinschaft (Project JoPTAG).     

The MacLaurin expansion for a G/G/1 queue with Markov-modulated arrivals and services

Consider a Markov-modulated G/G/1 queueing system in which the arrival and the service mechanisms are controlled by an underlying Markov chain. The classical approaches to the waiting time of this type of queueing system have severe computational difficulties. In this paper, we develop a numerical algorithm to calculate the moments of the waiting time based on Gong and Hu's idea. Our numerical results show that the algorithm is powerful. A matrix recursive equation for the moments of the waiting time is also given under certain conditions.     

空间有源消声实验研究

本文分别在半声室和普通房间进行有源消声实验，证明了作者以前提出的算法是收敛的，稳定的，以此算法构成的多通道有源消声系统对直达声和混响声都能进行了有效的控制。     

人工神经网络在配煤过程状态建模中的应用研究

本文详细介绍了人工神经网络应用于状态建模的方法．对神经网络应用中的一些难点提出了切实可行且有效的解决措施，并举例作了应用示范．同时还介绍了神经网络方法应用于优化动力配煤的情况，并就神经网络方法在优化动力配煤中的进一步应用作了展望．     

最小风险证券组合的结构分析和迭代算法

本文分析了最小风险组合证券投资的结构特征，并提出了一种组合证券风险最小化的迭代算法，证明了其收敛性．该算法操作简单，且易于处理不允许卖空情况下的证券组合问题．     

Renormalization Approach to Optimal Limiter Control in 1-D Chaotic Systems

Optimal limiter control of chaos in 1-d systems is described by flat-topped maps. When we study the properties of this control by a bifurcation analysis of the latter, we find partial universal behavior. The optimality of the control method is expressed by an exponentially fast control onto selected periodic orbits, making targeting algorithms idle.