MatlabNNR

JPEG_LS无损压缩的算法，此算法经常用于高质量的算法研究，希望大家好好利用。也希望群主给我加下载点数。(JPEG_LS lossless compression algorithm that is often used in high-quality research algorithms, we hope to take advantage of. I also hope that the main group to add download points.)

This is a complex model of a wind turbine.

图像harris角点检测matlab源代码，使用说明(images harris corner detection Matlab source code, use)

变结构控制仿真实例源代码，《滑模变结构控制仿真研究》随书光盘(Variable Structure Control simulation source code, " Sliding Mode Variable Structure Control Based on" CD with the book)

matlab图形处理,可处理灰度,彩色图形,对图形进行各种变换,添加噪音,滤波处理等(matlab graphics processing, processing grayscale, color graphics, various changes to the graphics, add noise filtering process)

hilbert spectrum and DFT

matlab实现流行识别，用于多相流的测量(matlab to achieve popular recognition for the measurement of multiphase flow)

从神经元电极上测得的原始信号中检测锋电位信号(detect spikes)

说明：  此程序是计算电力系统潮流的通解，通过支路法不断更新参数(This procedure is the general solution of the power flow calculation, continuously updated through the branch method parameters)

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 )