运动侧边界海洋问题的自适应网格模拟方法

文中给出了普遍形式的变动界运动学条件,通过对其进行的量阶分析可知,在宽阔潮间带少域采用该条件是必要的.推导出了任意运动曲线坐标系下普遍适用的二维浅海流体动力学方程组和可变边界运动学条件,从而方便地采用随岸界的运动而自动伸缩的自适应网格方法,来模化具有运动边界的浅海动力学问题.用此模型数值模拟了1969年的一次风暴潮,特别包含了黄河三角洲海域的风暴潮漫滩过程.     

时空联合估计权向量的MVDR自适应波束形成方法

为实现短数据条件下权向量的稳定优化估计,提出1种时空联合估计权向量的MVDR自适应波束形成方法。该方法结合时域解析信号的MVDR自适应波束形成算法中构造时域解析信号的方法和直接数据域算法中空间滑动的方法,以减少一半权向量为代价,实现了在更短的数据长度下稳定优化地估计协方差矩阵和权向量。数值仿真实验和海上实验数据处理结果表明:与常规波束形成和直接数据域相比,该方法具有更好的稳定性和更好的波束性能,即主瓣更窄,旁瓣更低,阵增益更高。     

一种新型混沌遗传优化算法的研究

在对遗传算法交叉、变异后结果的处理方法进行改进并在将进化过程分段的基础上,提出1种新的混沌遗传算法。该算法利用混沌运动的遍历性择优产生初始群体,对每一轮遗传操作所得到的部分当前最优个体进行变尺度混沌搜索。仿真结果表明,该算法具有较高的优化效率,并能求得全局最优解。     

港区堤防布置的遗传算法优化

为探讨港口投资的最大效益，利用遗传算法来解决港区堤防布置的优化问题。针对问题的特性设计了目标函数、编码方式和适应度计算，选用了适当的选择、交叉和变异操作，数据结果显示该方法具有良好的搜索性能。     

改进的遗传算法在地图匹配中的应用

介绍了影像导航中基于遗传算法的匹配方法,针对遗传算法在处理平行线时存在错误的问题,提出了基于距离角度拟合的算法,并用实验进行验证,结果表明:改进的算法可以较好的纠正遗传算法的错误.     

基于鲁棒Hausdorff距离和遗传搜索的快速匹配方法

采用SIFT算子提取特征点,减少图像数据量,针对传统Hausdorff距离对噪声、出格点较敏感的问题对其进行改进,并以改进后的鲁棒Hausdorff距离作为匹配测度,利用非遍历而又有效的遗传搜索策略进一步提高了匹配速度。对发生旋转变形和灰度变化的遥感影像进行模拟实验,实验结果证明了该算法的有效性和快速性。     

Genetic Algorithms of Structural Fuzzy Reliability Index

In this paper the simple generation algorithms are improved.According to the geometricmeaning of the structural reliability index,a method is proposed to deal with the variables in the standardnormal space.With consideration of variable distribution,the correlation coefficient of the variables andits fuzzy reliability index,the feasibility and the reliability of the algorithms are proved with an example ofstructural reliability analysis and optimization.     

BP网络学习参数模糊自适应算法的实现

前馈神经网络BP算法的改进方案中,对网络训练(学习)过程中学习率和惯性系数进行模糊自适应调节,以提高收敛速度,是一项很有效的措施。文中具体分析了如何根据设计者的先验知识确定模糊规则和隶属函数,并以三比特异或函数(或称奇偶分类)的实现为例,验证了这种算法的改进、加速了BP网络的学习过程。     

基于最小均方误差算法消除GPS接收机噪声

从解决GPS接收机受干扰的实际需求出发,阐述了基于LMS算法的自适应滤波器基本原理和不同的噪声产生方式,并对GPS接收机仿真信号与噪声进行叠加。最后用基于LMS算法的自适应滤波器对仿真信号进行消噪处理,得到良好的消噪效果,证明了基于LMS算法的自适应滤波器能够应用于GPS接收机滤波系统。     

一种用于认知无线电资源分配的并行免疫遗传算法

提出了用于认知无线电自适应调制和资源分配的并行免疫遗传算法,并对该算法、简单遗传算法和静态调制分配算法进行了仿真.仿真结果显示,该算法具有很强的全局搜索能力和较快的收敛速度,在误码率和功率受限条件下,该算法比简单遗传算法和静态调制方式的性能更好,同时明显降低了计算复杂度.     

Geoacoustic Inversion via Genetic Algorithm and Its Application to Manganese Sediment Identification

Abstract

An acoustic inversion method using a wide-band signal and two near field receivers is proposed and applied to multiple layered seabed models including a manganese sediment. The inversion problem can be formulated into a probabilistic model comprised of signals, a forward model, and additive noise. The forward model simulates wide-band signals, such as chirp signals, and is chosen to be the source-wavelet-convolution plane wave modeling method. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the possible numerical ranges for a priori uniform distribution is based. The genetic algorithm is applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂ norm of the difference between measured and modeled signals. Not only the marginal pdf but also its statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm.     

A New Method of Automatic SVP Optimization Based on MOV Algorithm

We applied the maximum offset of sound velocity algorithm to sound velocity profile streamlining and optimization to overcome multibeam survey and data-processing efficiency problems. The impact of sound velocity profile streamlining on sounding data accuracy is evaluated. By automatically optimizing the threshold, the reduction rate of sound velocity profile data can reach over 90% and the standard deviation percentage error of sounding data can be controlled to within 0.1%. The optimized sound velocity profile data improved the operational efficiency of the multi-beam survey and data postprocessing by 3.4 times, indicating that this algorithm has practical value for engineering applications.