FEM2D_hoanchinh

MATLAB相关书籍，希望对大家有用，相互学习，相互促进(MATLAB-related books, we hope useful, learn from each other and promote each other)

基于Matlab对表面肌电信号时频域分析处理的GUI界面程序(Surface EMG frequency domain analysis of the GUI interface program based on Matlab)

一个变步长仿射投影算法，包括论文及仿真代码，已调试通过(A variable step-size affine projection algorithm, including the papers and the simulation code, has been debugged)

一个入门级别的模糊控制simulink仿真模型，感兴趣的同学可以下载学习(Fuzzy control of an entry-level the simulink simulation models, and interested students can download learning)

通过根据统计特性建立的模型，编写FM广播信号的模糊函数(By model based on statistical properties of the preparation ambiguity function FM broadcast signal)

optxf filter in the matlab

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 )

系统深入得多，一些在第一本中只略微论及的高级内容，这里讲得完整透彻。理论与实践并重，可操作性强。例题都有可直接运行的matlab文件，正文中也给出了关键部份方便阅读。读完这本书，住真水平可以上一个台阶，基本上可以达到系统级仿真的能力。(System is much more depth, some in the first book deals with only a slight premium content here put a complete and thorough. Theoretical and a practical, easy to operate. Example are directly run matlab file, the text also gives a key part of user-friendly. After reading this book, the level of living can be really a new level, but basically can achieve system-level simulation capabilities.)

说明：  支持向量机的实验程序，对支持向量机进一步研究(SVM experimental procedures, further research on support vector machines)

分类器的训练与学习是模式识别的一个重要环节,其目的在于按照某种算法,确定判决规则,使之具有自动分类识别的能力。本文介绍了采用Parzen窗法的随机模式分类器,并matlab实现了一个简易的随机模式分类器。(Classifier training and learning is an important part of pattern recognition, in accordance with the purpose of some kind of algorithm to determine the decision rules to identify with the ability of automatic classification. This paper introduces the Parzen window method using a random pattern classifier, and achieved a simple matlab random pattern classifier.)