很好用的支持向量机优化算法例程代码

本程序用于求解层次分析法中的特征值问题。正互反阵最大特征值和特征向量实用算法，本程序为求和法。(This procedure is used to solve the ahp eigenvalue problems. Is reciprocal array maximum eigenvalue and eigenvector practical algorithm, the procedures for the methods of summation by. )

book10ebychapters chen

该仿真程序以倒立摆数学模型为例，用于求系统传递函数、传递函数的极点以及开环脉冲响应。(The simulation program pendulum mathematical model, for example, is used the demand system transfer function, the transfer function poles as well as the open-loop impulse response.)

matlab实现单尺度retinex算法，实现图像增强。(matlab achieve a single scale retinex algorithms for image enhancement.)

Face recognition in MATLAB code by Sanjeev Singh

说明：  ISOMAP代码，附数据，用于材料的缺陷检测(ISOMAP code with data for defect detection of materials)

不同的速度..............二例.....在移动网络的4G LTE的切换

本程序运用bing yang的PAST算法和PASTD算法跟踪信号子空间，并利用MUSIC算法估计信号的波达方向(DOA)(This procedure utilizes the PAST algorithm and the PASTD algorithm (put forward by Bing Yang)track signal subspace, and uses MUSIC algorithm to estimate signal prameter Direction Of Arrival(DOA) )

系统辨识作业，输入信号为M序列，利用互相关函数及脉冲响应法求出系统的传递函数。(Identification of the operating system, the input signal for the M series, the transfer function of the system is obtained using cross-correlation function and impulse response method.)

经验模态分解（Empirical Mode Decomposition，简称EMD))方法被认为是2000年来以傅立叶变换为基础的线性和稳态频谱分析的一个重大突破?，该方法是依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。 该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数（Intrinsic Mode Function，简称IMF），所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理，然后进行希尔伯特变换获得时频谱图，得到有物理意义的频率。(The Empirical Mode Decomposition (EMD) method is considered as a major breakthrough in the linear and steady state spectrum analysis based on Fu Liye transform in 2000. The method is based on the time scale characteristics of the data itself to decompose the signal without setting any base functions in advance. The key of this method is the empirical mode decomposition, which can decompose the complex signal into a limited eigenmode function (Intrinsic Mode Function, for short, IMF), and the decomposed IMF components include the local characteristic signals of the different time scales of the original signal. The empirical mode decomposition method can smooth the non-stationary data, and then obtain the time-frequency spectrum by Hilbert transform to get the physical meaning frequency.)