qiantuihuidaifa

说明：  Matlab code for RS encoding and decoding

电力系统日负荷曲线分析GUI程序。考虑不同类别的用户，以及最大负载值、平均负载在指定的期间内的统计分析。(GUIof power system daily load curve analysis algorithm. Consider the different categories of users, and the maximum load value, the average load over a specified period of the statistical analysis.)

自己变得额matlab 贝叶斯分类程序 实现图像分类 遥感图像分了(bayes classification for remote sensing image )

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

滤波器的设计 主要是设计一个滤波器处理原始的心电信号，是信号波形得到处理(heart signal filter and design .the main is design a filter to process the original heart signal.)

高压输电线的工频电场预测mathlab代码 最低高度11米对地1.5m的工频电场的预测(Power frequency electric field is calculated)

本文为 Stephen J.Chapman《MATLAB 编程（第二版） 》英文影印版的中文译本，这本 书对初学者很好的入门教材。之前从网上下载到邢树军兄弟译的 pdf版本(MATLAB-from fresh to expert.MATLAB Programming for Engineers)

这是一个接收机系统，详细的给出了信号的产生，调制，发射，接收，解调等环境，很好的例子(This is a receiver system, the signal is given in detail the generation, modulation, transmit, receive, demodulation and other environment, a good example)

Quaternion library for inertial navigation system

《MATLAB神经网络30个案例分析》中的第18个例子，案例18 Elman神经网络的数据预测—电力负荷预测模型研究。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of example, 18 cases of 18 Elman neural network research data to predict- power load forecasting model. Hope to have certain help to everybody! )