read_location_data

MATLAB 7.0 学习ppt，我学习MATLAB就是用这套ppt的，感觉很好，推荐一下。(MATLAB 7.0 learning ppt, I learn MATLAB is to use this ppt, and feel good, recommended it.)

独立分量分析的一种快速算法，适合初学者学习。(Independent component analysis of a fast algorithm, suitable for beginners to learn.)

风力发电最大功率点跟踪，采用爬山法，matlAB自带永磁同步电机，buck电路降压(Wind power maximum power point tracking, using climbing method, matlAB own permanent magnet synchronous motor, buck buck circuit)

灰度变换增强程序 1.灰度变换增强程序 2.直方图灰度变换 3.直方图均衡化程序举例 4.直方图规定化程序举例 1.线性平滑滤波 2.中值滤波器 3. 4邻域8邻域平均滤波算法 1.低通滤波器 2.布特沃斯低通滤波器图像实例(Enhanced gray-scale transformation procedure 1. Enhanced gray-scale transformation procedure 2. Histogram gray-scale transformation 3. Histogram equalization procedure for example 4. Histogram specification procedure for example 1. Linear smoothing 2. Median filter 3.4 8 neighborhood neighborhood average filter algorithm 1. low-pass filter 2. Butewosi Image examples of low-pass filter)

this code is in matlab 2009 and can is for bin packing problem with first fit next fit and best fit

无功补偿器statcom的仿真图，运行良好，可以改进欢迎提意见很分享(Var Compensator statcom simulation map, running well, you can improve it are welcome to share comments)

运用MATLAB进行空间自回归以及空间聚合程度计算，空间聚类的基本操作。(MATLAB is used for spatial autoregression and the calculation of spatial aggregation degree, and the basic operation of spatial clustering.)

DFA，MFDFA,分形去趋势分析和多重分形去趋势分析(Dfa, MFDFA(detrended fluctuation analusis,multifractal detrended fluctuation))

包括了Matlab 中实现数字信号处理、图像处理、小波分析和系统仿真等相关应用 领域的新技术，新算法.(Including Matlab, digital signal processing, image processing, wavelet analysis and simulation and related applications of new technologies and new algorithms.)

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)