rayleighbenard

显示原图，matlab.良好的操作性良好的操作性(Show the image, matlab. Good good interoperability interoperability)

Matlab for LLC resonant converter!

说明：  此代码是用matlab软件仿真PAM4信号的传输，自动生成传输数据，在接收端做基本的信号处理，计算出误码率。(This code uses MATLAB software to simulate the transmission of pam4 signal, automatically generate the transmission data, do basic signal processing at the receiving end, and calculate the bit error rate.)

《最优阵列处理》一书第七章的MATLAB例程，包括各种自适应波束形成算法。(" Best array processing," a book chapter VII of the routine, mainly adaptive beamforming algorithm.)

Fourier series in Matlab

一系列Matlab的m文件的打包，包括Hurst等(A series of Matlab m-file package, including Hurst, etc.)

matlab编写的Hausdorff距离程序(Hausdorff distance program written in matlab)

说明：  matlab 基于fir的低通滤波器 完整程序(matlab fir low-pass filter based on a complete program)

卡方检验用来检验分布的拟合性 比如正太分布对数正太分布高斯分布瑞利分布，韦伯分布等里面包含了对数据的检测以及统计的原理和方法(The chi-square test used to test the The chi-square test used to test the distribution fitting, such as too the distribution of the lognormal distribution is too Gauss points Burui Li distribution, Weibull distribution, which contains the principles and methods of detection and statistical data)

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 )