基于乐观策略的并行离散事件模拟研究

并行离散事件模拟（PDES）又称分布式模拟,通过将一个离散事件模拟程序在多个处理器上并行执行来提高模拟性能。乐观策略在解决并行模拟中各模拟部分之间的同步关系时具有较好的性能。介绍了基于乐观策略的并行离散事件模拟的原理,讨论了存在的问题,并给出相应的解决方法。     

时间偏差的并行离散事件模拟研究综述*

时间偏差是并行离散事件模拟中广泛使用的一种同步机制。从事件列表管理、消息取消、乐观性控制、状态保存和恢复、内存管理以及全局虚拟时间计算等几个主要方面对时间偏差的并行离散事件模拟进行了探讨,阐述了其存在的问题,并对各种优化策略进行了分析比较和对并行离散事件模拟的应用前景作了一个展望。     

时间偏差算法中通讯接口的研究及实现

时间偏差算法是一种实现并行离散事件模拟的协议,其目的在于利用并行平台并发地执行大规模离散事件的模拟,以缩短仿真时间。为保证结果的正确性,并行模拟必须解决同步问题。时间偏差算法利用虚拟时间来同步各模拟部分之间的执行,通讯接口是利用虚拟时间及回退策略实现同步的部分。对该部分进行了深入的研究,提出了一种优化的实现方案,并对其性能进行了分析。     

并行离散事件仿真中增量状态保存技术研究

并行仿真技术广泛应用于复杂系统仿真以及实时仿真中,回退是影响乐观并行仿真性能的关键因素.首先阐明回退与乐观同步算法的关系,然后讨论了回退中的增量状态保存技术,最后对其进行了建模和理论分析,并通过实验测试了增量状态保存策略的性能.     

并行离散事件模拟的同步机制研究

逻辑模拟在设计新系统的过程中起着重要作用,通过计算机进行模拟可以实时反馈输出结果,及早发现潜在的问题,进而缩短设计周期,降低研发成本。并行离散事件模拟通过分散计算量到并行机或者网络的多个节点来减少模拟时间,被视为解决模拟速度问题的有效途径。在影响模拟性能的因素中,各并行子系统之间的同步问题是直接影响并行性能的关键因素之一。探讨了并行离散事件模拟的同步机制,介绍了其基本原理、特点及存在的问题,并阐述了可能的改进方法。     

并行与分布式仿真时间推进同步机制综述

并行分布仿真对复杂大规模动态系统的研究以及探索,对长远的应用提供了便利,目前已成为研究的热点。然而时间同步管理是关注并确保并行分布仿真正确执行的一种机制,它不但要确保正确的执行因果逻辑关系,而且要保证仿真系统的可重复性。时间管理是影响并行分布仿真系统高效运行的重要因素之一。在分别论述四类基本的时间推进同步机制,即保守机制、乐观机制、混合机制和自适应机制的同时,还分析了各自的优缺点,指出了今后的研究发展方向。     

基于PDES的弹性周期同步算法研究

并行分布仿真对复杂大规模动态系统的研究以及探索其长远的应用提供了便利,近年来日益成为研究的热点.然而时间同步管理是关注并确保并行分布仿真正确执行的一种机制,它不但要确保正确的执行因果逻辑关系,而且要保证仿真系统的可重复性.时间管理是影响并行分布仿真系统高效运行的重要因素之一.文章首先指出了常用乐观时间同步机制固有的缺点;其次重点描述了事件推进限这一重要概念及弹性周期同步算法的设计与实现;最后对弹性周期同步算法的性能进行了较为深入的探讨.首先对该算法进行了定性的综合评估,随后详细讨论了弹性周期同步算法的关键性能参数M的定量理论分析与推导.     

并行仿真中全状态保存技术研究

乐观时间同步机制能够显著提高并行仿真的性能,但是在乐观时间推进过程中会出现因果错误,需要根据保存的状态对事件进行回退,因此事件状态保存机制是影响乐观时间推进效率的重要因素。本文首先简要介绍了并行仿真中逻辑进程的执行过程,讨论了乐观时间推进的状态保存与回退机制;然后对全状态保存技术进行了建模和理论分析,并通过实验测试了全状态保存算法的性能,测试结果验证了理论分析的正确性。     

基于并行遗传算法的规则发现研究

阐述了传统遗传算法的基本思想、原理和步骤及其在数据挖掘(规则集发现)中的应用,给出了基于遗传算法的知识规则挖掘算法的基本思想和关键问题,包括知识规则表示、适应度函数定义等,继而提出多种群并行进化结构,利用精英重组策略,产生池进化模型以及自适应参数的手段调整并行遗传算法进行数据挖掘。在算法具体实现过程中,采用了动态变异交叉概率等方法,有效避免了并行遗传算法中早熟现象的发生。以北美香菇数据为例,进行并行遗传算法挖掘分类规则,实验说明了该算法在发现和进化规则方面的有效性。     

PDES中乐观时间同步的时空损耗研究

并行离散事件仿真对复杂大规模动态系统的研究以及探索其长远的应用提供了便利,近年来日益成为研究的热点。然而时间同步管理是影响并行离散事件仿真系统高效运行的重要因素之一。乐观的同步是采用检测和回退机制,允许逻辑进程积极的处理本地事件。一旦出现同步错误则利用回退机制从错误中恢复到较早状态,然后再恢复执行。这一切都是通过基于检查点状态保存重建机制来实现的,因而状态保存及状态重建必然伴随着时间和空间的损耗。该文深入研究了在乐观同步机制下,仿真执行时间和内存空间的损耗与检查点间隔之间的关系,并通过推理计算给出了检查点间隔的最优取值范围。     

一种改进的自适应全双工中继系统中继策略

提出了一种基于源节点到中继节点(SR)信噪比门限和中继节点最小自干扰的全双工中继策略,并在此基础上与SR最大信噪比的全双工中继策略相结合,构建了一种改进的自适应全双工中继系统。在对等功率分配条件下使用DF协议转发时对自适应全双工中继系统的中断概率进行了分析,结果表明 与自适应半双工中继策略相比,提出的自适应全双工中继系统的中断概率更低。与基于SR最大信噪比的全双工中继策略相比,一般情况下,当SR信噪比系数较大时,自干扰信号也较强,此时所提自适应全双工中继系统采用基于SR门限和最小自干扰的中继策略,中断概率较低;而在自干扰信号较小的情况下,该自适应全双工中继系统将采用基于最大SR信噪比的全双工中继系统的中继策略,能维持较好的中断性能。     

Problem Oriented Software Engineering: Solving the Package Router Control Problem

Problem orientation is gaining interest as a way of approaching the development of software intensive systems, and yet, a significant example that explores its use is missing from the literature. In this paper, we present the basic elements of Problem Oriented Software Engineering (POSE), which aims at bringing both nonformal and formal aspects of software development together in a single framework. We provide an example of a detailed and systematic POSE development of a software problem: that of designing the controller for a package router. The problem is drawn from the literature, but the analysis presented here is new. The aim of the example is twofold: to illustrate the main aspects of POSE and how it supports software engineering design and to demonstrate how a nontrivial problem can be dealt with by the approach.     

Adaptive control design under structured model information limitation: A cost-biased maximum-likelihood approach

Networked control strategies based on limited information about the plant model usually result in worse closed-loop performance than optimal centralized control with full plant model information. Recently, this fact has been established by utilizing the concept of competitive ratio, which is defined as the worst-case ratio of the cost of a control design with limited model information to the cost of the optimal control design with full model information. We show that an adaptive controller, inspired by a controller proposed by Campi and Kumar, with limited plant model information, asymptotically achieves the closed-loop performance of the optimal centralized controller with full model information for almost any plant. Therefore, there exists, at least, one adaptive control design strategy with limited plant model information that can achieve a competitive ratio equal to one. The plant model considered in the paper belongs to a compact set of stochastic linear time-invariant systems and the closed-loop performance measure is the ergodic mean of a quadratic function of the state and control input.     

基于自适应PSO和类别分解的多任务串行联盟生成*

现有的联盟生成方案多针对一个agent只能加入一个联盟,不利于联盟总效用的最大化以及联盟中agent能力的充分利用。提出了基于能力类别的agent分解策略,通过定义子agent使得agent可以同时加入多个联盟,在此基础上设计了基于二维离散粒子群的多任务串行联盟生成算法,并对粒子的惯性权重进行动态自适应调整;最后通过算例验证了该方法的有效性。     

Advanced level‐adaptive overdrive (ALAO) method applicable to full‐HD LC TVs

Abstract— In this paper, we report on the development of an advanced level‐adaptive overdrive (ALAO) method applicable to full‐HD LC TVs, which makes it possible not only to reduce the gray‐level response time to less than one fourth, but also to improve the S/N ratio for still images by 10 dB; and further to reduce the circuit cost for the LAO method to almost half that of the prior LAO circuit. We have applied the ALAO method to a full‐HD LC front projector with 1.84 Mpixels and a screen size larger than 100 in., and we obtained high‐quality full‐HD moving images.     

Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints

This article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.     

基于自适应与主动消息的任务调度策略研究与实现

在并行分布计算中，任务调度策略是影响并行分布计算性能的重要因素，结合现有任务调度法存在的问题，运用有效聚合与充分释放的思想，提出并实现了一种具有自适应和主动消息的任务调度策略，有效地提高了系统的整体性能。     

Full Adaptive Integral Backstepping Controller for Interior Permanent Magnet Synchronous Motors

This paper examines the speed control issue for fully unknown Interior Permanent Magnet Synchronous Motors (IPMSMs). A full adaptive controller is proposed to control the speed of these motors while all physical parameters, adaptive variation bounds, and the load torque are unknown. Applying a four‐step backstepping strategy, which constructs the infrastructure of the whole controller design, and utilizing proper Lyapunov functions leads to generating proper adaptive control rules. The desired control performance is basically satisfied by rejection of the load torque and motor uncertainties. Optimizing the performance criteria, integral tracking error signals have been taken into account in the backstepping procedure in order to enhance the efficiency, robustness, and reduced steady‐state errors. The closed‐loop system stability is analyzed and proved utilizing Lyapunov and Barbalat lemmas. To evaluate the high performance of the designed controller, an illustrative example is simulated whereas the obtained results confirm the efficient treatment of the proposed method for the fully unknown system. The comparison of the simulation results with one of the recent papers in the literature verifies the effectiveness of the proposed method.     

自适应惩罚策略及其在交通信号优化中的应用

针对约束优化问题的求解,设计了一种处理约束条件的自适应惩罚策略,用于将具有不等式约束和等式约束的优化问题转变为仅包含决策变量上、下限约束的优化问题。该策略通过引入约束可行测度、可行度的概念来描述决策变量服从于不等式约束和等式约束的程度,并以此构造处理约束条件的自适应惩罚函数,惩罚值随着约束可行度的变化而动态自适应地改变。为了检验该惩罚策略的有效性,针对单路口交通信号优化问题进行了应用研究,并用三种不同算法进行了大量的仿真计算,结果表明所设计的自适应策略在具有高度约束条件的城市交通信号优化问题中具有良好的效果。     

自适应策略的混沌局部搜索遗传算法

提出了一种自适应策略的混沌局部搜索遗传算法（ACLSGA）,它是遗传算法中每一代的所有个体经过一次遗传操作之后得到一个最佳个体,通过自适应策略决定是否在最佳个体附近进行混沌局部搜索。4个基本的测试函数优化结果表明：ACLSGA比具有精英保留选择机制的实数编码的遗传算法（RGA）的全局搜索能力强,收敛速度快。