3

说明：  给出了利用OFDM实现UWB的源代码及实例。包括从信源、信道、接收等，是一个完整的系统，接口方便，容易学习使用。(given by using OFDM UWB achieve the source code and examples. Letter from the source, channel, the receiver, is a complete system interface easy to learn to use.)

weight trajectories for two different initial conditions an step-size( variance of u, denoted by var_u, can be changed. Also, the values of the step-size, in the variable mu, and the value of the initial weight estimate, in the variable w_init, can be changed.)

《MATLAB神经网络30个案例分析》中的第21个例子，案例21 LVQ神经网络的分类——乳腺肿瘤诊断。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of example, 21 cases of 21 LVQ neural network classification of breast cancer diagnosis. Hope to have certain help to everybody! )

对海杂波的仿真，考虑了相控阵雷达威力范围、随着距离增大而引起的强度衰减、杂波类型、对海杂波的抑制等多个方面，对雷达总体设计人员有一定的帮助，值得学习和借鉴。(simulate sea clutter with consideration of many aspects, such as sphere of action of PAR radar, strength attenuation resulted from extending range, type of clutter and inhibition of sea clutter)

notch filter specific design in matlab

这是与之前上传的Extract_test_IMFCC里面的一个子函数，pudn程序不能一次上载几个函数，只好分开(This is one subfunction of Extract_test_IMFCC uploaded before.Since pudn.com cannot upload several functions, they must divided.)

伪谱法优化软件，采用高斯伪谱法，适宜于解算飞行器轨迹优化问题(gopos 5.2)

支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。 (Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network )

Program that implements echo watermarking. The basic idea is to embed a watermark by adding an echo to the original sample.

聚类算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。 这里是MAtlab源代码。(err)