New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory: information system discrete algorithm (algorithm 1) and samples representatives judging algorithm (algorithm 2). On the principle of the farthest distance, algorithm i transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.     

Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems

In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted ...     

Adaptive learning algorithm based on mixture Gaussian background

The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.     

Modelling of a Class of Nonstationary Time Series with Kalman Filter Using Moving Window

In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalmnan filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kahnan filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.     

An anti-main-lobe jamming algorithm for airborne early warning radar based on APC-SVRGD joint optimization

Main lobe jamming seriously affects the detection performance of airborne early warning radar.The joint processing of polarization-space has become an effective way to suppress the main lobe jamming.To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming,a joint optimization algorithm based on adaptive polarization canceller(APC)and stochastic variance reduction gradient descent(SVRGD)is proposed.First,the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established,and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel.Second,based on the stochastic gradient descent principle,the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation,the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect.Third,by setting up a planar polarization array simulation scene,the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed,and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.     

Method of Chaotic Prediction Based on Wavelet BP Network and Its Application

The advantage of artificial neural network and wavelet analysis are integrated through replacing the traditional S-shaped activation function with the wavelet function. One method of chaotic prediction based on wavelet BP network was put forward based on the reconstruction of state space. Training data construction and networks structure are determined by chaotic phase space, and nonlinear relationship of phase points was established by BP neural networks. As an example, the new method was applied on short term forecasting of monthly precipitation time series of Sanjiang Plain with chaotic characteristics. The results showed so higher precision of the method had that the theoretical evidence would be provided for applying the chaos theory to study the variable law of monthly precipitation.     

Robust iterative learning control for nonlinear systems with measurement disturbances

The iterative learning control (ILC) has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.     

Robust H∞ control for networked control systems

The robust H∞ control for networked control systems with both stochastic network-induced delay and data packet dropout is studied. When data are transmitted over network, the stochastic data packet dropout process can be described by a two-state Markov chain. The networked control systems with stochastic network-induced delay and data packet dropout are modeled as a discrete time Markov jump linear system with two operation modes. The sufficient condition of robust H∞ control for networked control systems stabilized by state feedback controller is presented in terms of linear matrix inequality. The state feedback controller can be constructed via the solution of a set of linear matrix inequalities. An example is given to verify the effectiveness of the method proposed.     

Krylov subspace method based on data preprocessing technology

The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.     

基于PDES的最小时间桶算法研究

混合策略在一定程度上避免了过分保守或极端乐观的缺点。首先分析了现有同步机制存在的不足,然后论述了视界概念及最小时间桶算法的设计与实现,接着对最小时间桶算法的性能进行了定性定量的深入探讨,最后采用PHOLD仿真应用模型在MTB算法、保守算法和TW乐观算法之间进行了性能对比实验,实验结果表明：MTB算法在某些条件下具有更小的回退开销和更快的事件处理效率。

Abstract：

Hybrid strategy to some certain extent avoids the shortcomings of the excessive conservation and extremely optimism. The shortcoming of the existing time synchronization algorithms was analyzed; then the very important conception of the event horizon,furthermore was proposed,and the design and the realization of the Minimum Time Buckets Algorithm were put forward. The remainder provided the farther research for the performance of this algorithm by qualitative analysis and quantitative analysis. Finally,the PHOLD simulation model was used to compare the performance of conservative algorithm,TW algorithm and MTB algorithm. The experiment results indicate that under some conditions MTB algorithm has lower rollback costs and faster event process speed,which lead to better performance than TW and conservative algorithms.     

基于XVL技术的海底地形生成方法

XVL技术提供了大压缩比的不规则表面高精度简化和快速渲染的方法,可提高基于Web环境三维仿真的实时性。文中描述了在下一代三维引航系统中构建XVL格式的三维海底地形的方法,对于移动式、低带宽条件下的高逼真度三维仿真系统的实时性有重要的意义。经过水深数据提取、三角网构建和Lattice表面拟合等步骤后,生成的紧凑的、包含丰富细节的Lattice表面,能够大幅度地减少网络数据的传输量和提高客户端的渲染速度。采用从二维电子海图中提取海底地形数据的方法,充分地利用了珍贵的现有权威性资源,是一种快速构建海底地形的有效方法。本文还详细介绍了在VRML文件中扩展XVL标识,存储和表示海底地形数据的方法。     

一种基于数字地球的运动模型与地形匹配方法

三维模型与地形的匹配问题涉及到模型控制矩阵与模型与地形接触点高程值的计算。其中接触点高程值的计算问题与地形渲染采取的数据组织和绘制算法密切相关,采用规则网格构建地形的数字高程模型,在此基础上提出了弱网格实现数据结构的地形匹配算法。首先为三维模型在三维GIS视景中建立了数学控制匹配模型,然后基于规则网格像素坐标系计算模型与地形接触点的高程值。方法不考虑规则网格的具体绘制实现技术,在采用规则网格的地形下具有较强的适用性。     

基于FFT的海底地形二维匹配技术

海底地图匹配技术是水下地形辅助导航的核心技术之一,它主要用来提高水下载体(AUV3的定位精度,提出了基于付立叶变换的海底地形二维匹配算法(FFT算法),通过FFT变换将频域中幅值引入匹配,设计了位置匹配圆板进行实时图位置匹配与方向搜索模板进行方向搜索.应用实际海图原始数据进行仿真,结果表明,该算法能够同时匹配出位置信息和方向信息,具有较高的匹配精度和抗差特性.     

基于半参数回归的联合定轨误差估计仿真算法

通过对联合定轨非线性影响因素特征的分析,提出了一种参数建模与非参数分量表示相结合的联合定轨非线性半参数回归建模方法。首先讨论了联合定轨模型及常值系统误差估计算法,在此基础上建立了一种广义非线性半参数回归联合定轨模型,设计了相应的卫星轨道参数、系统误差参数及模型误差估计算法,并从理论上证明了半参数模型轨道估计精度优于经典最小二乘轨道估计精度。仿真计算结果表明,广义非线性半参数回归联合估计方法能将模型误差和随机误差有效分离,同时联合定轨精度也得到了进一步的改善,从实际应用角度验证了半参数回归联合定轨建模方法的合理性和可行性。     

基于DEM和SAR的航海雷达地形回波仿真

由于仿真地形数据库制图工作量巨大,因此目前航海雷达仿真中物标雷达反射率模拟的工程应用受到极大限制。提出基于DEM(Digital Elevation Model)和同波段SAR(Synthetic Aperture Radar)数据反演,获取物标反射率信息,作为地表雷达反射率仿真的依据,结合雷达方程,建立航海雷达地表反射率模型;考虑雷达回波地形的高程遮挡关系,给出了航海雷达地形回波生成算法,GPU编程实现。通过与传统仿真方法和真实雷达回波图像的比较,表明该算法与真实雷达回波更接近,更能体现地表回波图像的雷达反射率细节和不同量程下的回波图像变化。     

一种基于卫星照片的大地形仿真方法

提出了一种大地形制作方法,该方法采用多边形网格剖分算法由高程数据生成地形轮廓模型,以卫星照片作为贴图生成地形纹理,采用LOD技术提高地形显示速度。给出了卫星照片获取、卫星照片与地形轮廓匹配、贴图精度控制等关键技术的实现算法。结合某导弹飞行仿真项目,制作了某地区一万平方公里区域的大地形场景,仿真效果良好,验证了制作方法的有效性。     

基于随机模型的动态调度算法研究

针对多资源云环境中虚拟机放置问题,提出了一种在随机模型下综合利用率较高的动态调度算法MIUS （maximize integrated utilization scheduling）. 首先,在调度中心建立一个虚拟的中央队列缓存用户任务,然后利用随机路由算法对用户任务进行服务器快速选择,最后在服务器上建立虚拟等待队列并利用MIUS算法进行虚拟机配置. 仿真实验结果表明,该算法在保证QoS的情况下,实现了一定程度上的负载均衡,并可较大提高系统的综合利用率.     

基于地理坐标系的电磁环境快速仿真与体绘制

为有效考虑地球曲率的影响,采用球形规则网格模型组织电磁场数据。针对ITM(Irregular Terrain Model)模型计算量大的问题,提出一种地形重用方法提高仿真效率。针对多辐射源计算问题,将多线程计算和地形重用方法相结合,提高仿真效率。针对传统光线投射算法使用立方体作为包围盒,不能直接绘制球形规则网格数据问题,提出一种使用圆顶体作为包围盒的光线投射算法。经实验验证,考虑地形影响仍然能够获得较高的计算效率,满足多辐射源电磁环境仿真的需要,且圆顶体包围盒使光线投射算法的应用范围更加广泛。     

一种新的地景模型简化与快速绘制方法

地景模型是一类重要模型。它可以广泛应用在航空，航天与军事指挥等领域，但由于其数据量极大，如何实现快速绘制是一个难题，本文在分析地景模型数据特点的基础上，提出了地景模型简化的判决准则，并根据该准则提出了基于视点与图像分辨率的区域数据抽取与网格重构方法，以及基于法矢量（梯度）的模型简化及其LOD层次表示，最后进行了实验验证，结果表明，该方法数据压缩量大，绘制速度快，且逼真度无明显变化。     

基于核函数变换的PLS回归的非线性结构分析

基于核函数变换的PLS非线性回归模型既吸取了核函数能够拟合适应任意连续变化曲线的优点，又借鉴了偏最小二乘回归方法能够有效解决自变量集合高度相关的技术。在本文中针对多元加法模型，从理论和仿真试验的角度分别验证了，对于多个独立自变量对单因变量为非线性关系的数据系统，基于核函数变换的PLS回归方法不仅能够有效实现自变量对因变量的整体预测，而且能够提取各雏自变量对因变量的单独非线性作用特征，从而确定数据系统内部的复杂非线性结构关系，增强了模型的可解释性。