随机共振在微弱信号检测中的数值仿真

对基于非线性双稳系统随机共振的微弱信号检测技术进行数值研究,利用随机共振机制,浸入在噪声中的微弱信号可以得到有效的放大与增强。给出了基于Runge Kutta算法的双稳系统随机共振模型的求解方法,提出了利用随机共振检测微弱非周期信号的一种新思路。数值仿真结果表明,该方法不仅可以检测出强噪声极低频的微弱周期信号,而且可以对非周期信号进行有效的检测。     

战略联盟中知识转移影响因素概念模型构建：网络角度的思考

根据知识转移的一般过程,从社会网络视角出发,不同于以往研究中二元维度的分析,从网络结点能力、网络关系和认知、网络结构三个维度提出了影响战略联盟网络组织中知识转移绩效的关键影响因素,并深入分析各个关键因素的细分变量对知识转移绩效的影响作用机理,最后提出战略联盟网络知识转移影响因素的相关假设和概念模型,为下一步对假设的验证做铺垫。     

基于模糊Petri网的航材供应链冲突诊断建模与分析

结合航材供应链特点,建立航材供应链冲突诊断指标体系,并将节点问协作指标引入体系之中。通过模糊产生式规则,建立航材供应链冲突诊断模型,进行诊断推理,找出引起冲突的关键因素,并提出航材供应链冲突的防范对策。     

具有变时滞的离散型随机BAM神经网络的稳定性

研究一类具有变时滞的离散型随机BAM神经网络。通过构造Lyapunov泛函以及线性矩阵不等式（LMI）,得出了离散系统全局渐近稳定的充分条件。     

基于随机网络的平均维修保障服务时间分析

利用随机网络建模方法在随机工程项目分析中的优越性,建立了不同保障方式下的维修保障过程模型。重点对网络活动中的维修等待时间和维修等待概率进行了分析,利用随机网络的解析算法,确定了维修力量的平均维修保障服务时间分析模型。最后将该方法应用于伴随修理过程,对其平均维修保障服务时间进行了案例分析。     

基于Petri网的装备维修过程建模与AnyLogic仿真

针对装备维修重修复质量、轻过程质量,导致效率不高、浪费较重、缺乏过程管控等问题,建立了装备维修过程库所/变迁网模型和着色时间Petri网模型,给出了装备维修过程动态性、随机性、时间性描述,提出了不同修理技能人员工序完成时间的计算方法,运用AnyLogic软件进行了装备维修过程仿真,得出了不同人员组合所对应的不同维修时间和工序返工情况,为维修过程优化与验证提供了一种思路。     

天基信息支援下装备战场抢修有色Petri网模型研究

对天基信息支援下装备战场抢修活动过程进行了探索,运用Petri网建模工具建立了天基信息支援下装备战场抢修有色Petri网模型,将组织结构图、功能图和信息流动流程图集于一身,实现了系统的静态结构和动态过程的统一。模型直观、易于理解,对模型运行规则进行了详细说明,有利于仿真编程的实现。通过模型的深入研究,能够加深对天基信息支援下装备战场抢修规律的认识,进一步提高装备战场抢修能力。     

Approximating nondominated sets in continuous multiobjective optimization problems

Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or ε‐constrained problems, or discretizations‐based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation‐based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A generalization of the weighted set covering problem

We study a generalization of the weighted set covering problem where every element needs to be covered multiple times. When no set contains more than two elements, we can solve the problem in polynomial time by solving a corresponding weighted perfect b‐matching problem. In general, we may use a polynomial‐time greedy heuristic similar to the one for the classical weighted set covering problem studied by D.S. Johnson [Approximation algorithms for combinatorial problems, J Comput Syst Sci 9 (1974), 256–278], L. Lovasz [On the ratio of optimal integral and fractional covers, Discrete Math 13 (1975), 383–390], and V. Chvatal [A greedy heuristic for the set‐covering problem, Math Oper Res 4(3) (1979), 233–235] to get an approximate solution for the problem. We find a worst‐case bound for the heuristic similar to that for the classical problem. In addition, we introduce a general type of probability distribution for the population of the problem instances and prove that the greedy heuristic is asymptotically optimal for instances drawn from such a distribution. We also conduct computational studies to compare solutions resulting from running the heuristic and from running the commercial integer programming solver CPLEX on problem instances drawn from a more specific type of distribution. The results clearly exemplify benefits of using the greedy heuristic when problem instances are large. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Optimal power control in a wireless network using a model with stochastic link coefficients

This paper addresses optimal power allocation in a wireless communication network under uncertainty. The paper introduces a framework for optimal transmit power allocation in a wireless network where both the useful and interference coefficients are random. The new approach to power control is based on a stochastic programming formulation with probabilistic SIR constraints. This allows to state the power allocation problem as a convex optimization problem assuming normally or log‐normally distributed communication link coefficients. Numerical examples illustrate the performance of the optimal stochastic power allocation. A distributed algorithm for the decentralized solution of the stochastic power allocation problem is discussed. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005