Fig563

本人收集的几十篇关于小波去噪的论文，对于各个领域研究去噪的朋友有很大帮助(I collected dozens of papers on wavelet denoising, for research in various fields Denoising friends are very helpful)

动量理论-叶素理论计算涵道螺桨诱导速度及拉力系数(failed to translate)

这是计算LEO/MEO星座中的卫星轨迹的程序(LEO/MEO satellite constellation track procedures)

说明：  离散信号处理举例，开发环境是MATLAB。(Discrete signal processing, for example, development environment is MATLAB.)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)

说明：  基于matlab的K均值聚类程序。其中用IRIS数据进行验证，得到了很好的结果。文件中包含了演示后的结果图(Matlab-based K-means clustering procedure. Which use IRIS data verification, have had good results. File contains the results of the demonstration plan)

利用各待评方案的指标体系，通过计算各待评价方案与理想解的接近程度，作为评价各方案的依 据的一种多目标决策评价方法(topsis sort)

polyfit demo program with noise added in(polyfit)

说明：  实现大地测量学中，椭球参数解算。（包括正解和反解）(Achieve geodesy, the ellipsoid parameters solver. (Including the positive solution and counter-solution))

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)