GM(1,1)模型的遗传算法优化及效果检验

对平稳的数据和非平稳数据两种数据序列建立的GM（1，1）模型，分别用加速遗传算法（AGA）和最小二乘法（LSM）对模型参数求解。结果表明：对平稳变化数据序列，两种方法建立的预测模型的拟合优度和预测精度无显著差异；对变化幅度较大的非平稳数据序列，基于AGA的GM（1，1）模型的拟合优度和预测精度高于基于LSM的GM（1，1）模型的拟合优度和预测精度。     

位场各阶垂向导数换算的新正则化方法

位场垂向导数大量应用于位场数据处理与解释中.当前广泛采用的位场各阶垂向导数换算方法为基于Laplace方程并结合波数域和空间域方法的具有递推特性的ISVD(integrated second vertical derivative)算法.本文在位场垂向导数换算的正则化方法和径向平均功率谱的基础上,提出一种位场各阶垂向导数换算的新正则化方法.新正则化方法仅需通过分析位场径向平均功率谱来确定一个截止波数,即可稳定换算位场各阶垂向导数.理论模型和实测数据实验结果表明:(1)新正则化方法物理意义明确、计算简单,且各阶垂向导数换算的稳定性和精度明显优于ISVD算法;(2)在用新正则化方法求得各阶垂向导数的基础上,利用泰勒级数法可以获得大深度、高精度的位场向下延拓结果.     

相遇蚁群算法在边坡非圆弧临界滑动面搜索中的应用研究

边坡非圆弧临界滑动面搜索是边坡稳定计算中的一个关键问题,其实质为安全系数最小的滑动路径搜索问题,采用效果良好的路径搜索算法--蚁群算法是目前研究的热点。为了克服传统蚁群算法效率低、效果差的缺点,基于蚂蚁正反向搜索相遇形成完整路径的原理,提出了一种相遇蚁群算法。将该算法用于边坡非圆弧滑动面搜索问题,提出了一种非圆弧临界滑动面搜索的新方法。通过2个边坡的算例计算及一个水库岸坡的工程应用,验证了新算法的有效性。计算结果表明,相遇蚁群算法无论是整个搜索范围还是从某一点起的搜索范围都要比一般蚁群算法大,所以相遇蚁群算法在搜索边坡临界滑动面时所得到解的多样性也要比一般蚁群算法好,因此,相遇蚁群算法的搜索范围能以较大的概率包含全局最优解,算法最终也能以较大概率搜索到全局最优解。最终,相遇蚁群算法可以在更大的范围内以更快的速度找到边坡的临界滑动面。     

参数优化LSSVM的巷道围岩松动圈预测研究

最小二乘支持向量机方法(LSSVM)在处理小样本、高维数、非线性的问题时,具有求解速度快、易于描述非线性关系的优良特性。但是,该方法得到的模型拟合精度和泛化能力依赖于其相关参数,因此,提出基于粒子群优化算法(PSO)的LSSVM参数优选方法。最后,用该模型对巷道围岩松动圈进行了预测研究。结果表明,PSO优化的LSSVM模型具有收敛速度快、计算精度高的特点,说明该模型是合理、有效的。     

GMS 5资料反演地表温度的一个修正算法

本文研究发展利用GMS 5/VISSR每小时卫星观测资料反演地表温度的方法,首先利用时空判断法进行云检测寻找晴空像元,然后从辐射传输方程出发,由实时探空资料求取大气上行、下行辐射率及大气透过率,根据由AVHRR NDVI导出的地表比辐射率,用单时相双光谱分裂窗法反演得到地表温度.比较反演结果与54511站及其他中国基准站2000年地面0cm地表温度实测值,相对于国际上其他经验公式而言,本文算法在精度上有所提高.敏感性分析试验着重于大气衰减的影响.基于本文算法,给出了内蒙中东部地区地表温度连续4天的变化实例以及东亚部分陆地“纯晴天”地表温度图.     

海底声学底质分类的ELM-AdaBoost方法

基于自适应增强算法（AdaBoost）结合极限学习机（ELM）,通过迭代、调整、优化ELM分类器之间的权值,从而构建了具有强鲁棒性、高精度的ELM-AdaBoost强分类器,增强了现有的ELM分类器的稳定性。以珠江口海区侧扫声呐图像为实验数据,对礁石、砂、泥3类典型底质进行分类识别,该方法的平均分类精度超过90%,优于单一ELM分类器的平均分类精度85.95%,也优于LVQ、BP等传统分类器,且在分类所耗时间上也远少于传统分类器。实验结果表明,本文构建的ELM-AdaBoost方法可有效应用于海底声学底质分类,可满足实时底质分类的需求。     

InSAR相位解缠算法的分析评价

相位解缠是合成孔径雷达干涉测量技术（InSAR）数据处理过程中的关键步骤,相位解缠算法的选取关系到InSAR最终成果数字方程模型（DEM）或者形变量的精度。介绍了相位解缠的基本原理,利用基于路径跟踪、最小范数和网络流思想的8种解缠算法对加拿大魁北克地区合成孔径雷达（SAR）干涉图进行相位解缠,通过不同的评价指标对解缠结果进行了评价比较。实验结果表明：Flynn最小不连续法、最小Lp范数法和Snaphu网络流法具有较好的稳定性和适应性。     

Mooring optimization of floating platforms using a genetic algorithm

This paper presents a new procedure for the optimization of the mooring design of floating platforms, in which an automatic design sequence is also established. Regarding the optimization philosophy, the following aspects are dealt with:

• The optimization of the platform heading and its mooring pattern, taking into account the environmental force spreading;

• optimum line length or line tension for each mooring line, associated to the optimization of the mooring line materials and sizes.

Basically, the main goal of this paper is to introduce a new method, which will provide the quickest way to find the best mooring system, defined here as that which minimizes platform responses.A genetic algorithm (GA) is applied in this contribution, and this paper describes exactly the procedure of developing a GA code directed toward the solution of mooring design optimization problems. In order to prove the efficiency and the vast potential of the proposed algorithm as a design tool, sample moorings are analyzed for different environmental conditions and the final results, including the time required to run them, are presented.     

Probabilistic approach for active control of structures: experimental verification

In our previous study (Earthquake Engineering and Structural Dynamics 2003; 32 :2301), we have developed a probabilistic algorithm for active control of structures. In the probabilistic control algorithm, the control force is determined by the probability that the structural energy exceeds a specified target critical energy, and the direction of a control force is determined by the Lyapunov controller design method. In this paper, an experimental verification of the proposed probabilistic control algorithm is presented. A three‐story test structure equipped with an active mass driver (AMD) has been used. The effectiveness of the control algorithm has been examined by exciting the test structure using a sinusoidal signal, a scaled El Centro earthquake and a broadband Gaussian white noise; and, especially, experiments on control have been performed under different conditions to that of system identification in order to prove the stability and robustness of the proposed control algorithm. The experimental results indicate that the probabilistic control algorithm can achieve a significant response reduction under various types of ground excitations even when the modeling error exists. Copyright © 2004 John Wiley & Sons, Ltd.     

The inversion of anelastic coefficient,source parameters and site respond using genetic algorithm

It gradually becomes a common work using large seismic wave data to obtain source parameters, such as seismic moment, break radius, stress drop, with completingof digital seismic network in China (Hough, et al, 1999; Bindi, et al, 2001). These parameters are useful on earthquake prediction and seismic hazard analysis.Although the computation methods of source parameters are simple in principle and the many research works have been done, it is not easy to obtain the parameters accurately. There are two factors affecting the stability of computation results. The first one is the effect of spread path and site respond on signal. According to the research results, there are different geometrical spreading coefficients on different epicenter distance. The better method is to introduce trilinear geometrical spreading model (Atkinson, Mereu, 1992; Atkinson, Boore, 1995; WONG, et al, 2002). In addition, traditional site respond is estimated by comparing with rock station, such as linear inversion method (Andrews, 1982), but the comparative estimation will introduce some errors when selecting different stations. Some recent research results show that site respond is not flat for rock station (Moya, et al, 2000; ZHANG,. et al, 2001; JIN, et al, 2000; Dutta, et al, 2001). The second factor is to obtain low-frequency level and corner frequency fromdisplacement spectrum. Because the source spectrum model is nonlinear function,these values are obtained by eye. The subjectivity is strong. The small change of corner frequency will affect significantly the result of stress drop.