借助Matlab遗传算法反演火山区压力源参数

首先分析了Mogi模型的经典最小二乘反演由于出现矩阵奇异或接近奇异而发散的局限性,决定采用具有全局高效搜索性和优化计算时不需要求导的遗传算法来反演火山区压力源参数。反演程序调用Matlab7.0 Release14工具箱中的选择、交叉和变异等相关程序。最后应用该程序和长白山天池火山区水准和GPS观测资料联合反演火山区压力源参数,经过比较遗传算法和阻尼最小二乘方法的反演结果,可看出2种方法结果近似,可见此方法方便快捷地实现了目标函数的最小化。     

（BN）n及SiB（n-1）Nn（n=2-11）稳定结构的研究

利用遗传算法及Gastreich提出的经验势函数,研究了（BN）n（n=2-11）和SiBn-1Nn（n=2-11）团簇的可能稳定结构,对能量较低的异构体在HF/STO-3G水平进行优化,得到了（BN）n（n=2-11）和SiBn-1Nn（n=2-11）团簇稳定结构单环及线状结构,并进一步分析了单环的结构特征及相对稳定性。     

求Ramsey数最优下界值的递归算法

要确定每个具体的Ramsey数的数值是相当困难的,至今人们只求出了为数很少的几个Ramsey数的数值.人们在研究Ramsey数性质的同时,也在估计Ramsey数的数值,得出了某些Ramsey数的下界值,但工作进展缓慢.本文提出了一种计算Ramsey数最优下界值的递归算法,该算法利用当今关于Ramsey数的最新结果,能得出Ramsey数的目前最优下界值.1 算法描述不妨将本算法定名为G,参数个数为1个以上(可变化),算法允许递归调用,其输出值为Ramsey数的目前最优下界值.C(k_1,k_2…,k_n)表示以k_1,k_2…,k_n作为输入,通过算法G所得到的输出结果,即C(k_1,k_2…,k_n)表示的是G算出的Ramsey数N(k_1,k_2,…,k_n;2)的目前最优下界值,其中N(k_1,k_2…,k_n;2)的含意与文献[2]中有关含意相同.算法G:     

一种最短路径的演示算法

根据最短径问题的理论 ,编制了一个最短路径问题的演示算法 ,文中给出了合理的存储结构及主要算法 ,本算法与教材中的算法不同 ,更好地体现了迪杰斯特拉思想     

A High Speed Detection Scheme for Point Targets in a Multitarget Environment

The purpose of this paper is to develop a high speed detection scheme for moving and / or stationary point targets in a multitarget environment as registered in an IR image sequence. An iterative approximate 3-D line searching algorithm based upon the geometric representation of lines (for non-maneuvering targets in space) in a 3-D space is derived. The convergency of the algorithm is proved. An analysis is performed of the theoretical detection performance of the algorithm. The statistical experiment results show high effectiveness and computational efficiency of the algorithm in the case of low SNR. The idea may be employed to satisfy the real-time processing requirement of an IR system.     

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.     

长春市主要广场城市森林结构的研究

通过样地调查对长春市主要广场的城市森林结构进行分析得出：广场森林的密度为160株／hm^2，广场共有乔木树种52种，花灌木30种。树木的平均胸径为21．6cm，树木的总叶面积为739172．8m^2，叶面积指数为1．2，广场森林总生物量为5174．2t，Shannon-Wiener指数平均为1．7。从以上分析可以看出长春市主要广场园林植物种类丰富，并已达到森林水平可以发挥出很大生态效益。     

Seismic 3D Prestack Time Migration on Parallel Computers

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The Young-Householder algorithm and the least squares multidimensional scaling of squared distances

It is shown that replacement of the zero diagonal elements of the symmetric data matrix of approximate squared distances by certain other quantities in the Young-Householder algorithm will yield a least squares fit to squared distances instead of to scalar products. Iterative algorithms for obtaining these replacement diagonal elements are described and relationships with the ELEGANT algorithm (de Leeuw 1975; Takane 1977) are discussed. In large residual situations a penalty function approach, motivated by the ELEGANT algorithm, is adopted. Empirical comparisons of the algorithms are given.An early version of this paper was presented at the Multidimensional Data Analysis Workshop, Pembroke College, Cambridge, July 1985. I want to thank Jan de Leeuw and Yoshio Takane for bringing the ELEGANT algorithm to my attention and for clarifying its rationale and notation. My thanks go also to Stephen du Toit for help with the ALSCAL computations reported in Section 7.