bootspolit.m

This is simple lmi buliding algorithm with lmi control toolbox.

Kmeans.m对UCI数据集中的鸢尾花数据集进行kmeans算法聚类, Iris.txt 鸢尾花数据集 KMeans.m 鸢尾花KMeans聚类算法代码 test1.m，test2.m 鸢尾花算法测试集 包含数据的归一化 DataNorm.m数据归一化代码，DataStandard.m数据标准化代码 恩达老师的MATLAB视频中的代码 code(Kmeans.m clusters the kmeans algorithm for the Iris dataset in the UCI dataset, Iris.txt Iris dataset KMeans.m Iris KMeans clustering algorithm code test1.m, test2.m Iris algorithm test set Normalized data containing dataNorm.m data normalization code, DataStandard.m data normalization code The code in the MATLAB video of Teacher Enda the code is the code in the video.)

使用matlab写的类似windows的扫雷游戏，个人认为是目前最好的版本了 很推荐很赞(Writing a similar use of matlab' s mine windows games, personally think that is the best version of a very Zan Recommend)

Minimum Error thresholding method realized in Matlab environment based on Kittler and J. Illingworth, "Minimum error thresholding," Pattern Recognition, vol. 19, pp. 41-47, 1986.

求不规则波导的散射参数的matlab程序，使用的matlab的微分工具箱（有限元法），总的来说还不错，附带实验报告。(Scattering parameters of irregular waveguides matlab program, using a differential matlab toolbox (finite element method), on the whole pretty good, with a test report.)

The file introduces a modelling for the photovoltaic array panel.

Proportional-Integral-Derivative (PID) control is the most common control algorithm used in industry and has been universally accepted in industrial control. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity, which allows engineers to operate them in a simple, straightforward manner (exp. ref:national instruments). This file analyse different conditions and methods for tuning the PID controllers.

说明：  精通MATLAB优化计算算法，无约束多维极值问题。共11个程序。(Proficient in MATLAB optimization calculation algorithm, unconstrained multi-dimensional extremum problem. A total of 11 procedures.)

kmean clustering algorithm. this code will take the training data set (with out class lebel) as input and return the no of optimum centre used in rbfn by using eucleadian distance.

同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。(At the same time the height and weight data as features, Fisher with linear identifying method for classifier, will this classifier applied to training and test sample, to investigate the training and test error conditions. Will the training sample and values of the decision on the graph shows the boundary, and at the same time before the Bayes method with optimal classifier also paint chart, the similarities and differences of the comparison results. )