高分辨率雷达目标一维距离像的编码识别算法

文中提出了一种高分辨率雷达目标一维距离像的识别算法。该算法借用图像处理方法,先对雷达目标的一维距离像进行编码,经过傅里叶变换提取一组形状特征。这组特征精确地描述了一维距离像曲线的走向。而后,利用人工神经网络技术对一定姿态角变化范围内的这组形状特征进行识别。实验结果表明,可以获得良好的识别效果。该算法为雷达目标一维距离像识别算法的实时处理提供了一条有效途径。     

基于遗传算法的协同多目标攻击空战决策方法

多机协同多目标攻击是未来空对空作战的一种重要形式。首先建立了多机协同空战的自主优势矩阵 ,并依据多人冲突决策理论构造了空战的总体优化指标向量 ,然后针对其他优化算法的不足 ,提出用遗传算法优化该指标向量 ,实现多机协同多目标攻击空战决策 ,最后对 2∶ 2空战进行了仿真。仿真结果证明了上述思想的正确性。     

A competitive genetic algorithm for resource-constrained project scheduling

In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998     

Hamilton系统辛算法的Nekhoroshev稳定性分析

研究辛算法应用于Hamilton系统时的Nekhoroshev稳定性，利用辛映射的插值定理，得到了辛算法的插值定理，进而把它应用到凸Hamilton系统，得到了辛算法的Nekhoroshev稳定性，即辛算法在指数长时间内的稳定性。     

基于蚁群算法的被动目标定位算法

通过对蚁群算法的研究,用蚁群算法成功解决了无源测向定位中消除虚假点,对真实交点进行正确聚类的难题.并通过Matlab 仿真实验证明了该算法的有效性.同时该算法具有目标关联确率高、计算速度快、鲁棒性强等优点.     

Evolutionary determinants of war

This paper considers evolutionarily stable decisions about whether to initiate violent conflict rather than accepting a peaceful sharing outcome. Focusing on small sets of players such as countries in a geographically confined area, we use the concept of evolutionary stability in finite populations. We find that players’ evolutionarily stable preferences widen the range of peaceful resource allocations that are rejected in favor of violent conflict, compared to the Nash equilibrium outcomes. Relative advantages in fighting strength are reflected in the equilibrium set of peaceful resource allocations.     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

基于卫星云图历史资料反演云团非线性预测模型

针对线性预测方法难以有效描述云团的非线性、非平稳变化的困难,基于经验正交函数分解(EOF)和遗传算法参数优化结合的思想,提出了一条云团非线性预测模型反演的方法途径。首先将卫星云图序列作EOF的时、空分解;在此基础上,引入遗传算法对EOF的时间系数序列进行了动力模型重构和模型参数反演,建立了EOF时间系数的非线性微分方程组;再通过时、空函数合成,构造了云团演变的动力预报模型。试验结果表明,反演的云团预报模型能较为合理地描述特定季节区域内云团演变的基本趋势,预测结果与实际云图的主要特征基本相符,尤其是实现了云图3h以上的中、长时效的客观预测。     

人工免疫在军事领域的应用

人工免疫学是继神经网络、进化计算之后新的智能计算研究领域和研究热点.为探讨人工免疫可运用的军事领域,给相关科研工作者提供新方法、新思路,对生物免疫系统的一些概念进行了介绍,简要分析了生物免疫系统中的免疫机制,人工免疫系统基本特征,然后着重分析了该学科在信息系统安全、防御系统设计、目标识别、故障诊断、优化、火力分配等方面中的应用.     

基于遗传算法的雷达管控问题

通过对雷达组网系统责任区保障问题的深入研究,以完成对责任区的保障任务为目的,使责任区各个高度层均满足一定联合探测概率和雷达覆盖系数为约束条件,以雷达网整体工作代价最小为目标函数,构建了有干扰情形对雷达的管理和控制(以下简称雷达管控)数学模型。运用改进的遗传算法对雷达管控模型进行求解,通过设定保护概率保存了最优个体的遗传性,通过合理的算法参数设置改善了算法的性能。仿真算例和实际应用结果表明,雷达管控达标率由管控前的36.54%上升到63.46%,达到100%,雷达管控结果符合管控要求,且雷达开机数量较少,采取的抗干扰措施合理,验证了模型和方法的有效性。