Bigdata__743574SWITCHMAN_5525245

说明：  本书介绍雷达对抗原理，包括雷达对抗概述、雷达信号频率的测量、雷达的方向测量和定位、雷达侦察的信号处理、雷达侦察作用距离与截获概率、遮盖性干扰、欺骗性干扰、干扰机组成及干扰能量的计算以及对雷达的无源对抗技术(The book introduced the principle of radar confrontation, including an overview of radar confrontation, radar signal frequency measurement, the direction of the radar measurements and positioning, radar signal processing, radar detection and interception of the probability range, to cover sexual harassment, deceptive jamming, jammer component and interference with the energy calculation as well as against the passive radar technology)

基于matlab的车牌识别，采用的是BP神经网络来识别

基于ov5640 500万像素自动对焦摄像头 FPGA图像采集缓冲和显示 是学习FPGA图像视频处理的基础 内附ov5640使用手册

可用于支持向量机SVM的Matlab工具箱

modbus通用读取电表数据源码，使用的是stm32F103，已经用在工业上，稳定和移植都很好，不是用操作系统的。有疑问可以参考我的另外一个源码，都是通用的源码。

十万词汇英汉词典词库JSON格式,用心整理的一个词库，用JSON格式存储，直接可供JAVASCRIPT编程使用，适合制作在线或离线词典，或者背单词应用，注意只包含简要释义，不包含音标注音，更加精简节省存储空间。

QPSK通过Rayleigh信道多径衰落的Matlab仿真。

也是刚学的matlab，学习GUI编程，还是直接看程序，写代码比较好。看书吧，程序敲下来都有不通的。自己结合别人代码稍微改了下，训练和测试库都带了，界面简单明了，可以运行。为了像自己一样的matlab小白，我还写了一个运行指导。简单但是对做识别整个流程很清晰的。

pdf文档，中文来源：华北电力大学学报242007[11997199811.2199910.88408200.68000.47800.2掩76072007000100120时间/天68040100120图3训练过程中的误差变化时间/天Fig.3 Errors during training图1日负荷预测时洲练数据4Fig. 1 Training data for day load for ecas ting35←只51.5050-10203040如0的070809010101200.8时间压图4训练过程中神经元个数变化Fig 4 Grow th of neurons during train ing0.200.95系0.940.8505101520253035滞后时间/天如0.80.75图2训练数据的自相关系数Fig. 2 Autocorrelation coefficients of training dat a(a拟合曲线0.〔(3)10.040.0系70/14060[1000000]时间/天(b拟合误差「O1000001图5训练结果及误差Fig. 5 Result and error of train in3- EBF6,C1994-2012cHinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net4EUNITE2582080078076074072026.26700(3)66005101520253035时间/天50/1[00001图6日最大负荷预测结果[01010]26ig. 6 Result of forFrecasting g1 SOFNN[716.2,739.7,757.7,781.3],7Tab 1 Accuracy of training and forecast ing[720.1,738.2,763.7,MethodMAPE/ (%O) ME767.7],7SOFNN1.3344.137901.7850.04780EUNITE1.95E770760winner750报告中ME值没有准确堤供,但可以从报告中估轵740计得出约50~60完整的 EUNITE网络竟赛原始数据可730从EUNITE网站获得(http://neuron.tuke.sk/compe-720tit ion/ index. php)7103354EUNITEMAPE时间1.95[2]图7周平均最大负荷预测结果3.2Fig. 7 Forecasting result of week average max loadSOFNNSOFNNWSj72SOF NNWLWLideltaWli, delta WLi=WLj-Wli(3h:× delt al+k× deltal wj,Yh=0.58o1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp:/www.cnki.net262007820[1] Chen B J, Chang M W, Lin C J. Load forecasting using800support vector machines: a study on eunitE com-780tion 2001[J]. IEEE transactions on power systems7602004.19(4):1821-1830[2] Company behind East- Slov akia Power Distrib ution740Com panWorle w ide com petition w ithin the EU720NITE network, EUNITE competiton report [RI700[3]6802004.28(17):1-1105101520253035[4 Leng g, Prasad g, McGinnity T M. An on line algor-时间/天rithm for creating self organ izing fuzzy neural networ ks图8修正后的日最大负荷预测结果Neural Netw orks, 2004,(17): 1477-1493[5 Ort iz: Arroyo D, Skov M K, Huynh Q. Accurate Elee-F ig.8 Forecasting results after rev sedtricity Load Forecasting with Artificial Neur al NetworksIC. Proceedings of the 2005 International Conference2SOFNNon Compu tat io nal Intel ligence for M odel ling, Control andTah 2 Accuracy of forecast ingAuto mation. and International Conference on I ntelligentMethodMAPE/(%)MEAgents, Web Technolo gies and Internet CommerceSOFNN1.7850.04(CIMCAIA WTIC 05). 20051.5941.95[ 6] Tao X. Input dimens ion reduction for load forecastingEUNITE1.9550-60based on support vector machines [C]. Hong Kong82004 IEEE International Conference of Electric U til yderegulation, res tructur ing and pow er technolog iesMAPE ME20[7 Hsu CC. Dynam icall y Optim izing Parameters in SupportVector Regression An A pp licat io n of Electricity Load4结论Forecasting [C]. Haw aii: Proceedings of the 3 9thIlaw aii International Conference on System Sciences199918 Pan kratz A. Forecasting w ith Univariate Box-JenkinsModels [m. John Wiley sons, 1983SOFNN(1972-),,C1994-2012cHinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

MATLAB里面分别用了小波去噪，contourlet去噪，以及二者与循环平移结合的去噪,直接运行quzao.m就可以，操作方便，运行效果也好