水泥窑协同处置生活垃圾的燃烧特性分析优化

针对传统方法难以分析掺烧生活垃圾后的水泥窑复杂燃烧特性的问题,引入数据挖掘技术,以国内某水泥厂为对象,采集相关参数数据,使用稳定性选择算法分析各参数对煤耗与NO_x排量的影响系数,通过随机森林算法建立煤耗与NO_x排量的数学模型,结合K-means聚类算法得出关键优化参数及其最优值。结果表明,该方法能够建立精确的煤耗与NO_x排量模型,挖掘出节能减排的关键优化参数及其最优目标值。通过改善关键优化参数至最优值,能够大大降低煤耗与NO_x排量,可指导水泥厂优化窑内燃烧特性。     

基于参照点收益价值函数化的资产选择模型

诺贝尔经济学奖得主丹尼尔.卡尼曼及其合作者阿莫斯.特沃斯基的展望理论指出决策行为会受到价值函数形式的影响。本文首先介绍展望理论,给出若干具体的价值函数形式,在考虑投资者决策受价值函数影响的基础上建立基于对参照点收益价值函数转化的若干资产选择模型,从直觉上能够判断出这些模型更加符合投资决策心理和行为,而实证结果也表明这些资产选择模型能够有效地降低负离差风险。     

ISAR target recognition based on non-negative sparse coding

Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.     

Variational discretization for parabolic optimal control problems with control constraints

This paper studies variational discretization for the optimal control problem governed by parabolic equations with control constraints.First of all,the authors derive a priori error estimates where |||u-U₍h|||_L∞（J;L²（Ω））=O（h²+k）.It is much better than a priori error estimates of standard finite element and backward Euler method where |||μ-U_h|||₍L∞（J;L²（Ω））=O（h+ k）.Secondly,the authors obtain a posteriori error estimates of residual type.Finally,the authors present some numerical algorithms for the optimal control problem and do some numerical experiments to illustrate their theoretical results.     

导弹子母弹抛撒的均匀性指标的确定及评定方法

本文以导弹侵彻子母弹抛撒为背景,提出了一种适合于有限子弹抛撒的均匀性指标,并且运用现代统计方法——ＢａｙｅｓＢｏｏｔｓｔｒａｐ法模拟了均匀性指标统计量的分布;另外,从火力运用的观点出发,根据子母弹抛撒的均匀性指标的大小对战斗部毁伤效率的影响,确定了均匀性指标的容许下限;应用统计检验方法给出了子母弹抛撒的均匀性的评定方法,为导弹侵彻子母弹抛撒的鉴定及同类武器该项目的鉴定提供可信且便于操作的方法。     

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.     

一种复杂系统研究方法——基于多智能体的整体建模仿真方法

本文在对一般复杂系统进行研究分析的基础上,针对目前国内外的研究现状,提出了一种研究复杂系统的新方法－基于多智能体的整体建模仿真方法,对这种方法的操作流程和步骤进行了详细的说明,并介绍了支撑这种方法的ＳＷＡＲＭ仿真平台,最后指出了这种建模仿真方法在军事和经济系统中的应用前景。     

雷达大地回波反射和散射系数的测量

介绍了雷达大地回波特性的一种测量方法 ,包括测量系统框图和基于回波波形的数据处理方法。利用该方法得出了一种地面的反射系数和散射系数 ,该结果对雷达高度表的设计和其它地面反射和散射系数测量均具有参考价值     

C~3CM(C~2W)系统效能评价技术

通过分析研究建立Ｃ３ＣＭ系统效能评价准则,探索了Ｃ３ＣＭ效能评估技术和方法     

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.