基于支持向量机的威胁判断模型

目标威胁判断是防空作战中一项重要内容,在建立目标威胁模型时,首先要挑选特征参数,分析了影响威胁度的若干因素.这里采用Rough理论中知识约简方法选择目标的特征参数;支持向量机是一类新型机器学习方法,由于其出色的学习能力,该技术已成为当前国际机器学习界的研究热点,利用支持向量机建立了威胁判断模型,给出了实例和解决此问题的支持向量机源程序.通过实例与神经网络法的结果进行了比较,结果表明支持向量机比较精确和简单.     

Linear compound algorithms for the partitioning problem

For a given set S of nonnegative integers the partitioning problem asks for a partition of S into two disjoint subsets S₁ and S₂ such that the sum of elements in S₁ is equal to the sum of elements in S₂. If additionally two elements (the kernels) r₁, r₂ ∈ S are given which must not be assigned to the same set S_i, we get the partitioning problem with kernels. For these NP‐complete problems the authors present two compound algorithms which consist both of three linear greedylike algorithms running independently. It is shown that the worst‐case performance of the heuristic for the ordinary partitioning problem is 12/11, while the second procedure for partitioning with kernels has a bound of 8/7. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 593–601, 2000     

强化学习框架下移动自组织网络分步路由算法

移动自组织网络是一种无基础设施、由移动通信节点组成的无线网络,具有高动态特性。传统的路由协议并不能适应节点移动性带来的频繁拓扑变化,简单的洪泛路由也会因开销过大降低网络的性能。针对如何在移动自组织网络中自适应地进行路由选择,提出强化学习框架下的分步路由选择算法。该算法以最小链路总往返时延为目标,基于强化学习进行路由搜寻,在筛选出符合目标需求节点集合的基础上,结合置信度选择路由。在链路变得不可靠时,数据包被广播给筛选出的邻居节点集以提升路由可靠性并降低开销。对提出的算法在分组到达率和路由开销等主要性能指标进行数值仿真分析。仿真结果表明,提出的分步路由算法相比于基于强化学习的智能鲁棒路由,在降低开销的同时,保持着相当的吞吐率。     

在线学习的主用户仿冒攻击策略

在认知无线网络中,次用户通过频谱感知来学习频谱环境,从而接入那些没有被主用户占用的频谱空隙。事实上,多种恶意攻击的存在会影响次用户频谱感知的可靠性。只有深入研究恶意攻击策略,才能确保认知无线网络的安全。基于此,研究了一种认知无线网络中的欺骗性干扰策略,即主用户仿冒攻击策略,该攻击策略通过在信道上传输伪造的主用户信号来降低次用户频谱感知的性能。具体来说,将攻击策略问题建模为在线学习问题,并提出基于汤普森采样的攻击策略以实现在探索不确定信道和利用高性能信道间的权衡。仿真结果表明,与现有的攻击策略相比,提出的攻击策略能更好地通过在线学习优化攻击决策以适应非平稳的认知无线网络。     

空空协同作战指挥控制系统

论述了研究与发展空空协同作战指挥系统的必要性 ,探讨了空空协同作战指挥系统的概念及系统的建模、结构模式 ,指挥决策的制定与优化、人机关系的处理、作战效能的评估与通讯共享集等几个问题     

近似推理在水雷参数分析专家系统中的应用

为了尽快分析出未知水雷障碍参数,根据水雷战的特点,提出了建立未知水雷障碍参数分析专家系统的观点,对专家系统的设计方法进行了一定的探讨,并针对专家系统建立中的"瓶颈"问题,提出了基于Vague集插值近似推理的专家系统知识自动获取方法,在介绍推理过程的基础上给出了算例.从推理的结果来看,该方法具有较高的可信度,从而为专家系统的研制提供了一定的方法支持.     

基于科学计量的机器学习技术发展评估分析

机器学习是通过数据训练使机器获取新的知识和技能的计算机技术,是人工智能技术的核心和前沿领域之一。本文以Web of Science?核心合集收录的机器学习领域相关SCI论文和incoPat全球专利数据库收录的机器学习领域相关专利为数据源,运用科学计量方法对文献数据和专利数据进行时间、地域和机构分布分析,展示了机器学习领域研究实力的分布情况,并对机器学习的发展趋势进行了分析;然后利用主题聚类分析方法及可视化软件VOSviewer,挖掘出机器学习领域的关键技术、技术热点;最后对结果进行了总结分析,以期为我国机器学习发展布局提供参考和借鉴。     

Learning ‘Under Fire’: Israel’s improvised military adaptation to Hamas tunnel warfare

What organisational attributes enhance a military’s ability to effectively adapt on the battlefield? Upon the outbreak of war in July 2014 between Israel and the Palestinian militant group Hamas, the Israel Defense Forces (IDF) encountered an expansive network of tunnels from which Hamas was launching large-scale assaults into Israel. This article illustrates that the IDF’s ability to successfully adapt ‘under fire’ to this battlefield surprise was facilitated by several important attributes related to its organisational learning capacity: a dynamic, action-oriented organisational culture, a flexible leadership and command style, specialised commando units which acted as ‘incubators’ for learning and innovation, and a formal system to institutionalise and disseminate lessons learned.     

The military as a learning organisation: establishing the fundamentals of best-practice in lessons-learned

The post-Cold War era has witnessed the rapid expansion of organisational learning initiatives within NATO militaries, especially formal “lessons-learned” processes. The effectiveness of national lessons-learned processes in recalibrating military activity to the demands of ongoing operations has been highly-differentiated. However, the academic literature on military change and practitioner guidance has been slow to investigate the key features of best-practice in military learning. This article breaks new ground by drawing upon the literature on dynamic organisational capabilities to explore the fundamental organisational processes and activities which are necessary to implement successful lessons-learned. It examines, in particular, the organisational features which facilitate “knowledge transformation”: the effective combination of new knowledge gained from intra- and inter-organisational learning with existing organisational knowledge. The article concludes by highlighting several potential future empirical and theoretical research agendas in military learning and the importance of engagement between lessons-learned practitioners and organisational learning scholars.     

支持向量顺序回归机的线性规划算法

支持向量顺序回归机是标准支持向量分类机的一个推广,它是一个凸的二次规划问题。本文根据l1范数与l2范数等价关系和优化问题的对偶原理,把凸的二次规划转化成线性规划。由此提了支持向量顺序回归机的线性规划算法,进一步用数值实验验证了此算法的可行性和有效性。并与支持向量顺序回归机相比,它的运行时间缩短了,而且误差i不超过支持向量顺序回归机;