第五章

Dark_Channel图像去雾，分割部分采用的是guide_filter而源文献中的Soft_Matting，运算效率有较大提升。输入文件地址及文件名需要自己修改。(Dark_Channel Haze Removal, using guide_filter other than soft_matting.)

matlab从文档中读取数据进行三次样条插值。(matlab to read data from the document, cubic spline interpolation.)

利用Matlab及施工过程中采集的点云数据绘制网格图(Point cloud data collected in the use the Matlab and construction process to draw the grid map)

卡尔曼滤波器预测，以及一周卡尔曼滤波器预测(Kalman filter forecast, and a week Kalman filter forecast)

1phase fault in induction motor

组合数学和图论中的概率方法 组合数学和图论中的概率方法.pdf(Combinatorics and graph theory probabilistic methods)

判断数据是否符合概率分布；内含多种概率分布(Determine whether the probability distribution of the data)

basic dsge model in dynare

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)

MATLAB程序设计教程电子教案 包含：matlab实例编程一百个源程序， MATLAB程式设计入门篇程式流程控制等一系列基本操作(MATLAB Programming Guide electronic templates Contains: matlab programming examples one hundred source, MATLAB Programming Beginners program flow control and a series of basic operations)