EXISTENCE AND UNIQUENESS AND STABILITY OF SOLUTIONS FOR STOCHASTIC IMPULSIVE SYSTEMS

This paper studies the existence, uniqueness, and stability of solutions for stochastic impulsive systems. By employing Lyapunov-like functions, some sufficient conditions of the global existence, uniqueness, and stability of solutions for stochastic impulsive systems are established. Furthermore, the results are specialized to the case of linear stochastic impulsive systems. Finally, some examples are given to illustrate the applications of our theory.     

用面向对象程序设计神经网络的算法类库

依据面向对象的程序（ＯＯＰ）设计的优越性,用Ｃ＋＋语言,针对神经网络ＢＰ算法和ＧＡ算法,编写了神经网络及其算法类库：目的是为神经网络学习算法的发展和深入及其应用于工程提供方便和有利工具．且给出利用该类库实现的非线性系统辨识结果     

一种通用的城市道路交通流微观仿真系统的研究

交通流仿真是研究交通问题的有效工具,本文采用复合概率、模糊逻辑等方法建立了描述交通流中不确定性因素、车辆的到达,在路段上行驶以及通过路口的微观仿真模型,并利用面向对象的方法设计实现了于种基于上述模型的通用城市交通流微观仿真系统,比较真实地再现了车辆在道路网络的行驶过程。     

广义线性系统的脉冲弱可观性

本文利用广义系统的系数矩阵,构造了它的不变子空间及其算法,在此基础上讨论了其广义弱可观性和脉冲弱不可观性。     

实时控制计算微分代数系统的代数约束算法

本文结合实际的航天工程背景,针对飞行器轨道约束实时控制模型问题进行了算法研究。首先分析了ＢＤＦ方法用于飞行器轨道约束实时控制模型问题时的缺陷,其后针对实际问题的特点构造了具有三阶收敛的代数约束算法,分析了该算法的数值稳定性,并对潜地式弹道约束实时控制问题及指标（ｉｎｄｅｘ）为２的单摆模型问题进行了实际仿真计算,理论分析以及数值结果表明代数约束算法对指标为２的半显式微分代数系统的实时控制计算是非常有效的。     

It型滞后随机系统的仿真

本文研究了Ｉｔｏ型滞后随机系统的数值解法与仿真,得到了Ｉｔｏ型滞后随机系统数值计算的迭代格式,分析了迭代格式的精度和用于Ｉｔｏ型滞后随机系统仿真的有效性,并用实例演示了所得方法的应用。     

一种更优的精度控制故障树解算方法

本文针对安全分析方法中故障树解算问题提出了一种完全依赖于所要求精度的近似算法——精度取舍法。详细给出了近似求解顶事件概率值的两个截断条件。结论表明,它在同时兼顾精度与时间的要求方面优于别的算法。     

随机系统利用变结构控制方法的镇定

本文研究了Ｉｔｏ型随机系统的变结构控制,构造了参数不具有噪声激励滑动流形与变结构控制律,对Ｉｔｏ型随机系统利用变结构控制方法给出镇定控制律,并用例子演示了本文结果的应用方法。     

Azimuth resolution improvement for spaceborne SAR images with quasi-non-overlapped Doppler bandwidth

The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar （SAR） ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be coregistered, while phase biases induced by topography, atmospheric propagation delays and baseline measurement errors should be calibrated. However, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler band-width. Furthermore, the method used to estimate phase biases from interferogram of azimuth prefiltered SAR image pairs wil fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band （TerraSAR-X） satel ite data validates the performance of the presented method.     

Multi-label dimensionality reduction and classification with extreme learning machines

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.