qam

function of dual sign algoritm

结构分析的有限元与MATLAB程序设计，主要介绍用MATLAB进行结构的有限元分析(Finite Element Structural Analysis with MATLAB Programming)

matlab程序画图，常见分形图，function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 (matlab program drawing, common fractal images function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 )

PCA成分分析检测程序代码，运用MATLAB编写，能够很好分析主要特征(PCA component analysis testing program code, written using MATLAB, to analyze the main features of good)

microphone eq in matlab. This program simulates the signal sounds.

介质层间反射系数及其透射系数的求解，此处是二分之一匹配层结构(Dielectric layer between the reflection coefficient and transmission coefficient of the solution)

image indexation using color histogram

说明：  蛙跳算法，基于蛙跳算法实现优化变分模态分解的K和a.(Frog jump algorithm, based on the frog jump algorithm to achieve optimal variational mode decomposition K and A.)

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：  采用谱分析的方法，通过拟合研究信号的长程相关特性..(long-range correlation using matlab...)