时变大滞后过程的仿人智能模糊控制

目前大滞后过程的控制问题仍然是公认的难题,并且许多被控过程都具有时变特性,即参数或结构随时间和环境的变化而改变,这更加大了控制的难度。通过深入分析仿人智能控制和模糊控制的优缺点,充分利用模糊控制不需要被控对象的数学模型、抗干扰能力强、鲁棒性好,和仿人智能控制抗滞后性强的优点,各取所长,提出了一种适合于时变大滞后过程的仿人智能模糊控制方案,并指出了其设计思路和步骤。仿真结果证明了该方案具有较好的动静态响应特性和较强的鲁棒性,为解决时变大滞后过程的控制问题提供了一种可行的方法。     

IEEE802．11协议下移动自组网节点自私行为研究

对IEEE802．11协议下移动自组网中节点的MAC层自私行为进行了研究,描述了自组网节点利用减少退避时间等方法占用媒体资源造成网络性能严重下降的问题。分析了节点自私行为对其退避时间、吞吐量、数据重传次数的影响。提出了一种基于退避时间、吞吐量、重传次数指标的节点自私行为检测方法,并提出了一种利用邻接节点信息表的节点自私度的检测和惩戒机制。实验证明以上方法能有效识别节点的自私行为,在一定程度上解决了节点自私行为问题。     

时间同步网的可生存性增强

研究大规模时间同步网的可生存性问题．时间同步网通过各节点的自主配置,拓扑结构不再是静态的、层次型的,而成为动态的网络结构,增强网络的可生存性．提出搜索世界的概念,针对时间同步网络建立了基于搜索世界的BA模型,并对模型进行优化,使时间同步网络的可生存性更高．动态的网络拓扑在部分节点失效以后受影响的节点能够自愈,给出了节点自愈算法,使节点自愈时对网络负载的影响比较小．     

Finite‐time stability of switched positive linear systems

This brief paper addresses the finite‐time stability problem of switched positive linear systems. First, the concept of finite‐time stability is extended to positive linear systems and switched positive linear systems. Then, by using the state transition matrix of the system and copositive Lyapunov function, we present a necessary and sufficient condition and a sufficient condition for finite‐time stability of positive linear systems. Furthermore, two sufficient conditions for finite‐time stability of switched positive linear systems are given by using the common copositive Lyapunov function and multiple copositive Lyapunov functions, a class of switching signals with average dwell time is designed to stabilize the system, and a computational method for vector functions used to construct the Lyapunov function of systems is proposed. Finally, a concrete application is provided to demonstrate the effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

数据挖掘在给水管网安全中的应用

简要介绍了数据挖掘及其在给水管网安全中的应用。提出了一种基于数据挖掘的时间序列相似性查询方法。通过对事故症候相似性查询，建立安全预警系统，为调度员提供事故预警信息。     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

基于小波的金融时间序列波动性研究

文章针对描述金融时间序列波动性的问题,提出了一种基于小波Mallat算法的时间序列分析方法,即先用Mallat算法对金融时间序列进行分解与重构,继而对各分解层上的单支重构分量进行时间序列分析。在实证分析中,以宝钢股份的股票收益率序列为例,对这种综合分析方法的有效性与准确度进行了验证,并得到了较为满意的结果。     

一种精确程序最坏执行时间分析方法

Java语言的动态特性使程序的最坏执行时间分析较悲观和难以预测,提出一种精确最坏执行时间分析方法,在高层分析中,引入一种标记方法,对带有标记的Java类文件进行反编译提取控制流程,得到每一个基本块中的Java 字节码指令的最坏情况下的执行次数,在底层分析中,建立结合流水线和高级缓存影响的时间模型,得到每条指令所对应的执行时间,最后结合高层分析和底层分析的结果得到程序的最坏情况下的执行时间。实验表明,该方法可以使对实时Java 程序的最坏情况执行时间预测更加安全和精确。     

Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.

Axiomatizing the Monodic Fragment of

As we can show that the two-variable monadic fragment of thefull first-order extension of the propositional branching time logic is not recursively enumerable, we show in thisarticle that the monodic fragment of can be axiomatized.