nangbie

说明：  本文描述了一种快速高效的二阶方法训练径向基函数网络(This paper describes a fast and efficient second-order method training radial basis function network)

Mallat算法分解与重构程序，用db4小波(Mallat algorithm for decomposition and reconstruction process, using DB4 wavelet)

说明：  人脸识别，用于人脸图片的检测领域，对于初学则有一定意义，作者来自麦吉尔大学(Face Recognition for Human Face Detection picture field, the beginner will have a certain sense, the writer from McGill University)

说明：  利用matlab对啁啾取样光栅的光谱的透射谱进行模拟(Chirped sampled grating using matlab on the spectrum of the simulated transmission spectra)

DOA估计，空间平滑MUSIC算法的MATLAB程序(DOA )

维纳噪声抑制；实验分析；实验结果及分析；利用信号之间的相关性进行信号估计，从而将噪声滤除(Wiener noise suppression Experimental analysis The experimental results and analysis Using the signal signal to estimate the association between, to filter out noise )

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

An m file for OSPF in Wireless sensor Network simulation.

MUSIC算法以及运用MUSIC、ESPRIT、GEESE进行非相关源的模拟测向并比较各种算法的性能(MUSIC algorithm performance and the use of MUSIC, ESPRIT, GEESE non-relevant source analog measurement and comparison of various algorithms to)

（1） 熟悉连续信号经理想采样前后的频谱变化关系，加深对时域采样定理的理解． （2） 熟悉时域离散系统的时域特性 （3） 利用卷积方法观察分析系统的时域特性． （4） 掌握序列傅立叶变换的计算机实现方法，利用序列的傅立叶变换对连续信号，离散信号及系统响应进行频域分析． ((1) familiar with the continuous signal to the manager before and after the sampling of the spectrum changes relations and deepen the time-domain sampling theorem understanding. (2) familiar with the time-domain system discrete time-domain (3) using deconvolution method of analysis in time domain characteristics. (4) master sequence Fourier Transform computer, using the Fourier transform sequence of continuous signals, Discrete signal system response and frequency domain analysis.)