黑马程序员c++就业班考试题

这是斯坦福的著名小兔子模型的点云数据（有ply和PCD格式），里面一共有6个模型。原数据下载地址http://www.cc.gatech.edu/projects/large_models/

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

LoRa(Long Range，远距离)是一种调制技术，与同类技术相比，提供更远的通信距离。由于LoRa调制是物理层（PHY），因此也可将其用于不同的协议和不同网络架构（如Mesh、Star、点对点）等等。

2007年全国大学生数学建模竞赛A题.zip

MSP430F5529 ADC12模块程序，包含单通道多次采样和多通道多次采样

nc 归一化相关系数 图像对比 图像相似 matlab

实验一：用MATLAB工具箱演示灰度调整，对比度增强，直方图均衡，局部平滑，中值滤波，频率域平滑与锐化，用MATLAB编写有关程序。 掌握知识点：灰度调整，对比度增强，直方图均衡，局部平滑，中值滤波，频率域平滑与锐化的原理与方法运用知识点：学会编写MATLAB函数来实现上述方法实验二：编写MATLAB函数实现最近临元法，双线性插值法掌握知识点：最近临元法，双线性插值法的原理和方法，运用知识点 若给出f(1,1) = 1, f(1,2) = 5 ,f(2,1) = 3,f(2,2) = 4,用上述编写的函数确定f(1.2,1.6)的灰度值实验三：huffman编码掌握

一份完整的毕业设计代码。包含数据库文件。开发语言为javaweb，基于ssh框架的系统

【实例简介】用于抑制音频信号的啸叫噪声，使用椭圆型函数设计陷波滤波器。

MAG3110电子罗盘代码基于STM32