chuongtrinh

先进pid matlab 仿真 刘金琨 第版 第一章程序 pid matlab simulink liujinkunc hap01_7.rar

基于SVM(支持向量机）的回归算法，很好用(Based on SVM (SVM) regression algorithm, very good use)

抢答器的实现功能的仿真，使用MATLAB进行仿真运行(Responder achieve functional simulation using MATLAB simulation run)

DGGE图谱的泳道的亮度（灰度）矩阵转化为0-1二维矩阵(DGGE profiles of the lanes of the brightness (gray) matrix into two-dimensional matrix 0-1)

fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function

This is an implementation of the algorithm described in our paper [1]. The input is any map generated by saliency detection algorithms like Itti-Koch [2] or GBVS [3]. The algorithm outputs a binary mask without requiring a threshold for the saliency map. More details about it are described in our paper.

在混沌研究中，求系统整体LE指数的matlab源代码(In the study of chaos, seeking overall system LE index matlab source code)

智能天线技术Matlab版本的源码，方便对照学习(Smart antenna technology versions of Matlab source code, easy to learn controls)

说明：  分簇协议fuzzy 亲测可以使用 代码很齐全 很有用 可以是通过MATLAB 直接是以哦那个(Clustering protocol, leach pro test can use a full range of codes, very useful can be directly through MATLAB)

本人修改过的elm的matlab算法，该算法比原来的elm的算法在超过3个神经元后的计算速度能明显加快！原理是运用函数产生列矩阵！！ ELM 是一种快速的神经网络算法，本人已经比较过，比很多流行的算法（BP,SVM）都快,而且效果很好，运行环境是matlab，可以测试所有的benchmark的数据(I modified elm of matlab algorithm elm than the original algorithm in more than three neurons after the computing speed can significantly speed up! The principle is to use functions to generate the matrix out! ! ELM is a fast neural network algorithm, I have a comparison, than many popular algorithms (BP, SVM) faster, and the effect of good operating environment is matlab, you can test all of the benchmark data)