Robust Adaptive Neural Control for a Class of Time‐Varying Delay Systems with Backlash‐like Hysteresis Input

This paper proposes a robust adaptive dynamic surface control (DSC) scheme for a class of time‐varying delay systems with backlash‐like hysteresis input. The main features of the proposed DSC method are that 1) by using a transformation function, the prescribed transient performance of the tracking error can be guaranteed; 2) by estimating the norm of the unknown weighted vector of the neural network, the computational burden can be greatly reduced; 3) by using the DSC method, the explosion of complexity problem is eliminated. It is proved that the proposed scheme guarantees all the closed‐loop signals being uniformly ultimately bounded. The simulation results show the validity of the proposed control scheme.     

欠定条件下弱稀疏源信号混合矩阵盲估计

针对源信号的稀疏性影响欠定混合矩阵的估计精度, 在源信号单源频率及非单源频率分量分析的基础上,通过对观测信号频率峰值的幅值比值所 构成的列向量聚类,提出欠定条件下弱稀疏源信号混合矩阵的盲估计方法。鉴于经典聚类算 法的局部收敛性带来聚类结果的不稳定性,采用全局收敛特性较好的遗传模拟退火聚类算法 提高聚类结果的鲁棒性。仿真实验表明,本文提出的混合矩阵估计方法及采用的聚类算法 在不同欠定条件及噪声环境下具有较强的估计性能。     

气体分馏装置的动态仿真

气体分馏装置主要由四个精馏塔构成,为实现该装置的动态建模,必须建立准确的精馏塔的数学模型。故结合物料衡算、能量衡算、相平衡及流体力学方程建立了精馏塔的动态数学模型,并应用于气分装置,通过C语言编程计算。将仿真结果与工厂实测值对比,证明上述的仿真值能较好地符合工厂实际情况。并对精馏塔做动态分析,在进料的阶跃干扰下,查看塔主要操作参数和产品组成的动态扰动,改进模型能正确预测精馏塔的动态特性,并且能在扰动下快速达到新的平衡。     

机载跟踪器的逃逸目标图像快速识别模型仿真

机载跟踪器进行逃逸目标识别跟踪的过程中,目标往往是高速移动的,为了逃逸跟踪,运动的速度和方向具有较大的突变性,与跟踪器之间会形成较大的跟踪距离差.传统的视觉方法为了弥补这种距离差,通过减少采集图像的样本,以提高跟踪的准确性,但是一旦样本数量过少,将会导致识别跟踪的准确性降低.提出基于高斯分布的动态逃逸目标识别跟踪方法.对跟踪目标图像进行归一化处理,获取与数据库中的样本图像具有同样的尺寸和分辨率的图像.计算动态目标图像的形心,根据高斯分布原理建立动态目标的识别跟踪模型,并对模型中的相关参数进行及时更新,保证了动态目标识别跟踪的及时性和准确性.实验结果表明,利用改进算法能够有效提高动态目标识别跟踪的准确性,缩短了识别跟踪的时间.     

玄武岩纤维混凝土物理力学性能试验研究

玄武岩纤维作为一种新型无机纤维材料,将其掺入普通混凝土中可显著提高混凝土的物理力学性能。结合前人研究成果,通过改变玄武岩纤维掺量,对玄武岩纤维混凝土的配合比设计、抗压强度、抗折强度、抗拉强度等力学性能,以及抗冻性。干缩耐久性等进行了试验研究,同时探讨了不同玄武岩纤维混凝土制备工艺对材料性能的影响。相关成果可为玄武岩纤维混凝土的工程应用提供理论依据。      

A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification

High-utility itemsets mining (HUIM) is a critical issue which concerns not only the occurrence frequencies of itemsets in association-rule mining (ARM), but also the factors of quantity and profit in real-life applications. Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database. Discovered HUIs may become invalid or new HUIs may arise when transactions are inserted, deleted or modified. Existing approaches are required to re-process the updated database and re-mine HUIs each time, as previously discovered HUIs are not maintained. Previously, a pre-large concept was proposed to efficiently maintain and update the discovered information in ARM, which cannot be directly applied into HUIM. In this paper, a maintenance (PRE-HUI-MOD) algorithm with transaction modification based on a new pre-large strategy is presented to efficiently maintain and update the discovered HUIs. When the transactions are consequentially modified from the original database, the discovered information is divided into three parts with nine cases. A specific procedure is then performed to maintain and update the discovered information for each case. Based on the designed PRE-HUI-MOD algorithm, it is unnecessary to rescan original database until the accumulative total utility of the modified transactions achieves the designed safety bound, which can greatly reduce the computations of multiple database scans when compared to the batch-mode approaches.     

Dynamic-cost-reward connection admission control for maximizing system reward in 4G wireless multihop relaying networks

The 3GPP Long Term Evolution (LTE) Advanced and IEEE 802.16j specifications adopt the mobile multi-hop relaying (MMR) mechanism for enlarging service area and improving wireless transmission quality simultaneously. By deploying different types of Relay Stations (RSs), MMR can bring some advantages: (1) the signal fading and wireless interference of a single long wireless link is improved obviously; (2) the ranges of wireless access and relay area are extended, etc. MMR can offer a high data rate transmission for packet services and can increase system capacity. Note that MMR can be applied to the public transportation system, e.g., equipped a mobile RS on a high-speed train. A mobile RS handoff initializes a multiple handoff requests of different types of traffics. It becomes as a critical handoff issue in 4G MMR. Thus, the MMR handoff needs a new efficient Connection Admission Control (CAC) to guarantee qualities for various types of traffics and to increase system revenue. However, traditional CACs are difficult to fulfill the objectives. This paper thus proposes the Dynamic Cost-Reward-based (DCR) CAC that consists of two key mechanisms: (1) adopting a Markov decision process-based (MDP) cost function and (2) providing different reward functions for different types of nodes and various types of connection. Additionally, a mathematical analytical Markov chain is modeled for DCR. The simulation results are very close to the analysis results, which justifies the correctness of the analytical model. Numerical results demonstrate that DCA outperforms the compared CACs in the probabilities of new blocking, MS-handoff, and RS-handoff dropping, FRL, GoS, and system reward.     

Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic optimization problems (DOPs). This is because once the algorithm converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to a new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution to address this problem is to increase the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants schemes. In this paper, an ACO framework for dynamic environments is proposed where different immigrants schemes, including random immigrants, elitism-based immigrants, and memory-based immigrants, are integrated into ACO algorithms for solving DOPs. From this framework, three ACO algorithms, where immigrant ants are generated using the aforementioned immigrants schemes and replace existing ants in the current population, are proposed and investigated. Moreover, two novel types of dynamic travelling salesman problems (DTSPs) with traffic factors, i.e., under random and cyclic dynamic environments, are proposed for the experimental study. The experimental results based on different DTSP test cases show that each proposed algorithm performs well on different environmental cases and that the proposed algorithms outperform several other peer ACO algorithms.     

基于制导链路的导弹干扰动态对抗仿真研究

随着计算机仿真技术的发展,系统仿真成为评估导弹武器作战性能的重要手段。以导弹中制导阶段制导受电子干扰的情况为主要研究对象,建立干扰信号功率的数学模型,阐述基于制导链路的动态对抗仿真实现方法。组成一套完整的导弹抗干扰动态仿真系统,为评估导弹受电子干扰的作战效果提供技术基础和实现途径。     

基于SimpleScalar的动态分支预测器研究

分支预测精度是影响当代处理器性能的重要指标,在近十年内一直是学术界和工业界的研究热点。为给不同应用场合的处理器动态分支预测器设计提供性能参考,针对处理器架构设计中应用广泛的几种动态分支预测器,使用SPEC CPU2000在SimpleScalar模拟器中进行仿真及测试分析。测试结果以预测精度和指令/时钟周期作为指标,并结合硬件开销,分析了不同种类分支预测器的适用对象和场合。