LMS_adaptive_filter

独立成分分析（Independent Component Analysis, ICA）是近年来发展起来的一种有效的盲分离技术，最早是由法国学者Herault和Jutten于1986年提出。ICA方法的提出最初是用来解决“鸡尾酒会”问题，其过程可以归纳为，在源信号与传输通道参数均未知的情况下，仅根据源信号的统计特性，出现测信号恢复出源信号。ICA分析的关键在于根据一定的优化准则建立描述输出信号独立程度的优化判据，即目标函数，并设计相应的优化算法，寻求最优的分离矩阵，使得输出信号中各分量尽可能相互独立。采用ICA方法对信号进行盲分离时，可避免过多地受大能量噪声成分的影响，将部分小能量信号源识别出来。随着ICA技术的不断发展，目前ICA技术已经广泛应用于语音增强、音频识别、通信、医学信号处理、金融数据挖掘、图像消噪、数字水印、遥感等领域，并取得了一定的研究成果。(Independent Component Analysis (ICA) Component ini order, developed in recent years is a kind of effective separation technology, originally from French scholars Herault and Jutten in 1986. The proposed method is initially ICA to solve the "cocktail" problem, the process can be divided into, in the source signal transmission channels and in the case of unknown parameters according to the source of the signals, and the statistical characteristics of recovering the source signal. According to the analysis of the ICA key lies in the establishment of optimal criteria described the output signal of the independent, objective function optimization criterion, and the corresponding optimization algorithm, the optimum )

几篇过于ESPRIT算法的英文文章,IEEE上的，感兴趣的话可以看看(ESPRIT algorithms are too few English articles, interested can look on the IEEE)

matlab环境下编写的求解等熵涡问题，属于CFD入门级代码(matlab environment prepared isentropic vortex problem solving, is the entry-level CFD code)

optimisation fuction matlab file

基于MATLAB的二元光学元件设计研究的源代码(MATLAB-based Design of binary optical elements of the source code)

信号的时移 y(t) = x(t - t0) 其中，t0为位移量。若t0为正数，则y(t)等于将x(t)右移t0秒之后的结果(This program is used to implement the time-shift operation )

【谷速软件】matlab有偿编程 裁剪Voronoi图 可以作为参考使用([Valley] matlab paid programming software speed cutting Voronoi diagram can be used as references)

数学交互界面，可以实现求解线性方程组，三维绘图，二重积分等(Math interactive interface for solving linear equations, three-dimensional drawing, the double integral)

利用DFT实现两轮信号相位差计算，包含谐波信号污染，白噪声信号污染分析(Phase difference between two signals to achieve the use of DFT calculations, the signal containing harmonics pollution, contamination white noise signal analysis)

智能微粒群为最小二乘支持向量机调参的示例程序（LSSVM+PSO）(Intelligent Particle Swarm and least squares support vector machine modeling(LSSVM+ PSO))