2.4Median-filtering-forensics-master

mat lab program for ac circuits

采用猫映射图像加密，有二维三维到四维。都是已经在matlab运行通过的(Cat mapping using image encryption, there are two-dimensional to three-dimensional four-dimensional)

说明：  这实际是一个通用的旋转正交经验函数。采用Kaiser标准化(Rotate EOF's This is actually a generic varimax routine and knows nothing special about EOFs. It expects a matrix of "loadings")

两路语音信号混合及解混的基于负熵的fastICA算法的MATLAB仿真实例(Two-way voice signals mixed and mixed solutions of negative entropy-based algorithm fastICA simulation of MATLAB)

MATLAB工具箱编写BP神经网络的非线性系统用于非线性函数拟合(Written MATLAB toolbox for nonlinear systems BP neural network for nonlinear function fitting)

星座图的画法 matlab 两种画法 仅供学习(constellation diagram )

调用MATLAB工具箱分解信号 小波的一维分解(Calling MATLAB toolbox decomposed signal one-dimensional wavelet decomposition)

该代码为电力系统区域间电力和能量交换，联络线功率控制的经典程序。(the code is made for power system energy and power exchange.)

ofdm papr simulation

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 )