PSORT

A High Pass filter matlab code for image processing in frequency domain

this is a simulink dc motor separate excitation with PID controller

实现目标形状的物体检测，该程序是从一对简单形状图形中提取出矩形(Goals shape object detection, the program is a pair of simple rectangular shape extracted graph)

数学形态学对信号进行去噪，效果较好，信号扰动特征得到很好保留(Morphology of the signal de-noising effect is good, the signal disturbance characteristics are well retained)

matlab 实现对整篇文章进行huffman压缩编码的源程序(matlab achieve whole article to the source huffman coding)

利用Matalb为工具，以粒子群算法来优化bp网络程序。 (Use Matalb as a tool to PSO to optimize the bp network program.)

Model Predictive Control exercise pt 2

详细介绍了如何使用matlab处理图形图像，让你轻松掌握GUI，清楚如何操作(Described in detail how to use matlab handle graphic images, allows you to easily grasp the GUI clear how to operate)

用matlab对模糊控制进行讲解，及其仿真的练习(Using matlab to explain to the fuzzy control, and simulation exercises)

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 )