ROCandAUC

一种用于Demosaic的方法，GBTF的matlab实现(Matlab code of a method of demosaicing, Namely GBTF)

Matlab教程 浅显易懂 推荐初学者学习(Matlab Tutorial for beginners to learn in simple recommendation)

批量处理NC文件的IDL pro 因为某些数据以月为单位存放，故利用这一程序可将对应的层次的时间序列进行整合，以便做进一步的气候统计分析(Batch NC file via IDL)

一个运用模拟退火算法求解TSP问题的小例子程序(A simulated annealing algorithm for TSP small example program)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )

说明：  模糊自整定MATLABA仿真程序，十分实用(Fuzzy self-tuning MATLABA simulation program is very useful)

捷连惯导的初始对准，超棒的对准matlab源程序，初学者不能错过，包括动静基座的对准(INS initial alignment of alignment, great matlab source, beginners should not be missed, including the movement of the base alignment)

fuzzy airplain control

its book to gui development for python

The DocPolynom to Character Converter The char method produces a character string that represents the polynomial displayed as powers of an independent variable, x. Therefore, after you have specified a value for x, the string returned is a syntactically correct MATLAB expression, which you can evaluate. The char method uses a cell array to collect the string components that make up the displayed polynomial. The disp method uses char to format the DocPolynom object for display. Class users are not likely to call the char or disp methods directly, but these methods enable the DocPolynom class to behave like other data classes in MATLAB. Here is the char method.