enginvalue_music

Simple function that given a matrix of exact values and a corresponding matrix of aproximated values determines diferent error types and the error percentage.

1 通过实验，掌握多元正态分布的最大似然估计； 2 掌握多元正态分布下的最小错误率的贝叶斯分类； 3 对其他的参数估计有更深的认识。 (1 experiment, master multivariate normal distribution maximum likelihood estimation 2 multivariate normal distribution under the minimum control error rate Bayesian classifier 3 on the other have a better understanding of parameter estimation.)

说明：  该仿真模型搭建了Buck-boost仿真模型，实现输入电压的变换(The Buck-boost simulation model is built to realize the input voltage conversion.)

该算法在原有的算法基础上提出的TDOA泰勒算法，可以用在传感器定位和蜂窝网定位。且该算法的定位精度比较高。(The algorithm is based on the original algorithm proposed TDOA Taylor algorithm can be used in sensor positioning and cellular location. And the algorithm is relatively high positioning accuracy.)

关于单载波频域均衡系统，判决反馈均衡的仿真。(On single-carrier frequency domain equalization using decision feedback equalization simulation material, and I hope to help you.)

matlab开发环境，．编程实现二维DFT的变换，并与标准函数结果fft2进行比较，基本傅里叶、离散余弦变换，空域（完成两个程序） 增强噪声后再进行处理，利用标准函数，求图像阈值分割、边缘检测的基本使用（lena256.bmp，方法：graythresh、edge），求分水岭，利用标准函数dwt2、idwt2分解图像man_pic.jpg，基本 BP-BPbase1 (matlab development environment. Programming dimensional DFT transform, and with the standard functions compare results fft2 basic Fourier, discrete cosine transform, airspace (to complete the two programs) to enhance the noise and then processed using standard functions for image thresholding, edge Detection of basic use (lena256.bmp, method: graythresh, edge), seeking watershed, using a standard function dwt2, idwt2 decomposed image man_pic.jpg, basic BP-BPbase1)

线性时变系统控制器设计的工具包，所用的方法是基于变化参数的李亚普诺夫线性不等式。每个m文件都有详细的说明。(Linear time-varying system controller design toolkit, the methodology used is based on changes in parameters of linear inequality Lyapunov. M files are each detail.)

Main.m为粒子群算法程序,属于优化类，data.txt为粒子群算法的数据文件(The Main.m particle swarm optimization procedures, belonging to the optimization class data file data.txt for the particle swarm algorithm)

说明：  以股票交易为例，用SVM网络回归预测，内有说明(SVM network regression prediction, with instructions)

说明：  由 得到幅频响应 。系统函数 由下式给出： （A.1） 若 ，则 为一全极点IIR系统，若 ，则 为FIR系统(By the frequency response obtained. System function is given by: (A.1) if, compared to an all-pole IIR system, if, for the FIR system)