Halpern-type iterations for strongly relatively nonexpansive mappings in Banach spaces

In this paper, we note that the main convergence theorem in Zhang et al. (2011) [21] is incorrect and we prove a correction. We also modify Halpern’s iteration for finding a fixed point of a strongly relatively nonexpansive mapping in a Banach space. Consequently, two strong convergence theorems for a relatively nonexpansive mapping and for a mapping of firmly nonexpansive type are deduced. Using the concept of duality theorems, we obtain analogue results for strongly generalized nonexpansive mappings and for mappings of firmly generalized nonexpansive type. In addition, we study two strong convergence theorems concerning two types of resolvents of a maximal monotone operator in a Banach space.     

基于简单特征值问题的修正GEPSVM

针对GEPSVM可能存在奇异性问题,提出一个改进的GEPSVM算法——SMGEPSVM。基于简单特征值问题,得到2个非平行过原点的超平面。与GEPSVM相比,SMGEPSVM不仅可以求解XOR问题,还可求解简单特征值问题,避免GEPSVM奇异性问题,测试精度好于GEPSVM,学习速度更快,其计算结果在人工数据集与UCI上得到了验证。     

杂草入侵ABC算法及其在控制分配中的应用

针对广义逆在进行控制分配时存在分配效率较低的问题,提出了一种新的控制分配方法.首先,利用人工蜂群算法(ABC)对伪逆阵进行寻优,选择较大的转矩可达集体积,从而提高系统的分配效率;然后,采用杂草入侵算法中的子代空间扩散机制和繁殖机制,分别取代了ABC中盲目性的进化方式和轮盘赌式的选择策略,解决了该算法可能出现收敛速度降低和陷入局部最优等问题;最后,通过仿真验证了提出的控制分配方法能有效地解决多操纵面飞机的控制分配问题.     

参数未知耦合时滞不同复杂网络的广义同步

针对具有不同动力学节点和不同拓扑结构的两个复杂网络,研究其广义同步问题,并考虑具有耦合时滞和参数未知的情况.基于Babalat引理,并利用Lyapunov稳定性方法,获得广义同步判据,设计有效的自适应控制器,实现混沌系统参数已知和参数未知两种情况下的广义同步.仿真验证了算法的有效性.     

Convergence Results of the Biconjugate Residual Algorithm for Solving Generalized Sylvester Matrix Equation

In this article, we investigate a variant of the biconjugate residual (BCR) algorithm to solve the generalized Sylvester matrix equation which includes the well‐known Lyapunov, Stein and Sylvester matrix equations. We show that the BCR algorithm with any (special) initial matrix pair can smoothly compute the (least Frobenius norm) solution pair of the generalized Sylvester matrix equation within a finite number of iterations in the absence of round‐off errors. Finally the accuracy and effectiveness of the BCR algorithm in comparison to some existing algorithms are demonstrated by two numerical examples.     

Robust Stochastic Stability and Control for Uncertain Singular Markovian Jump Systems with Multiplicative Noise

This paper focuses on the problems of robust stability and stabilization and robust control for uncertain singular Markovian jump systems with (x,v)‐dependent noise. The parameter uncertainties appearing in state, input, disturbance as well as diffusion terms are assumed to be time‐varying but norm‐bounded. Based on the approach of generalized quadratic stability, the memoryless state feedback controller is designed for the robust stabilization problem, which ensures that the resulting closed‐loop system has an impulse‐free solution and is asymptotically stable in the mean square. Furthermore, the results of robust control problem are derived. The desired state feedback controller is presented, which not only meets the requirement of robust stabilization but also satisfies a prescribed performance level. The obtained results are formulated in terms of strict LMIs. What we have obtained can be viewed as corresponding extensions of existing results on uncertain singular systems. A numerical example is finally given to demonstrate the application of the proposed method.     

基于改进型统一调度算法改善任务集的可调度性

实时系统要求任务在最差情况下能在其截止时间前获得结果,若超过了其截止时间,也会认为是错误的行为,所以改进任务可调度性分析、提高任务集可调度性尤其重要。统一调度能结合固定优先级调度的优点,防止不必要的抢占,降低资源额外销耗,能够提高任务集合的可调度性;但其任务的可调度性分析方法过于粗糙,影响任务最差响应时间分析的结果,降低了任务集的可调度性。针对存在的问题,基于统一调度,增加任务运行阶段数,重新建立任务模型,并提出通过分配任务抢占阈值、调整运行阶段的抢占阈值与长度,优化任务可容忍阻塞,改善任务集可调度性的算法。最后,实验表明,与统一调度算法及其他算法相比,所提出的调度算法能够有效改善任务集的可调度性。     

一种高效的多层和概化关联规则挖掘方法

通过对分类数据的深入研究,提出了一种高效的多层关联规则挖掘方法:首先,根据分类数据所在的领域知识构建基于领域知识的项相关性模型DICM(domain knowledge-based item correlation model),并通过该模型对分类数据的项进行层次聚类;然后,基于项的聚类结果对事务数据库进行约简划分;最后,将约简划分后的事务数据库映射至一种压缩的AFOPT树形结构,并通过遍历AFOPT树替代原事务数据库来挖掘频繁项集.由于缩小了事务数据库规模,并采用了压缩的AFOPT结构,所提出的方法有效地节省了算法的I/O时间,极大地提升了多层关联规则的挖掘效率.基于该方法,给出了一种自顶向下的多层关联规则挖掘算法TD-CBP-MLARM和一种自底向上的多层关联规则挖掘算法BU-CBP-MLARM.此外,还将该挖掘方法成功扩展至概化关联规则挖掘领域,提出了一种高效的概化关联规则挖掘算法CBP-GARM.通过大量人工随机生成数据的实验证明,所提出的多层和概化关联规则挖掘算法不仅可以确保频繁项集挖掘结果的正确性和完整性,还比现有同类最新算法具有更好的挖掘效率和扩展性.     

基于粒子群优化的隐式广义预测控制

提出一种基于粒子群优化算法（PSO）的隐式广义预测控制（IGPC）。滚动优化作为预测控制的重要组成部分对控制效果的好坏起着关键作用,因而寻求一种可靠的优化算法十分必要。PSO算法是一种群集智能方法,通过粒子之间的合作、竞争及进化实现对多维复杂空间的高效搜索,属于一类随机全局优化技术,已成功应用于各科学和工程领域。它将PSO算法应用于IGPC的滚动优化部分来求解最优预测控制律,仿真结果显示了该方法是可行的。