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于 2013-04-22 发布文件大小:1KB

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逐次订正法差值程序，可代替matlab自带的griddata(Successive correction method difference program, instead of Matlab comes griddata)

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电池测试数据
参考文献蓄电池等效电路模型，二阶RC等效电路模型(Reference to the paper on battery equivalent circuit model, second-order RC equivalent circuit model)

2019-04-28 21:32:01下载

积分：1
matlabtext2
考察温度x对产量y的影响，测得下列10组数据：求y关于x的线性回归方程，检验回归效果是否显著，并预测x=42℃时产量的估值及预测区间（置信度95 ）示例包含在内(X on the output of the temperature study of the impact of y, measured the following 10 sets of data: for y on x of the linear regression equation to test whether the effect of a significant regression and prediction when x = 42 ℃, and the valuation of yield prediction interval (confidence level 95 ) sample included)

2020-12-14 17:49:14下载

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Matlab-File-Exchange-Center
Matlab File Exchange Center的HHT的matlab代码。仅供参考。(HHT from Matlab File Exchange Center base on matlab)

2013-01-04 11:09:59下载

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mlclass-ex4
Programming Exercise

2013-02-07 09:12:56下载

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DVHOPSS
基于PSO的DV_hop算法，对定位系统有很好的效果，希望对你有用(The DV_hop algorithm based on PSO, the positioning system with good results, you want to be useful)

2011-10-23 20:36:07下载

积分：1
fractal
matlab程序画图，常见分形图，function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 (matlab program drawing, common fractal images function cantor(A,B) c=1 p=0 figure hold on plot([A(2) B(2)],[A(1) B(1)], r* ) while norm(A(1,:)-B(1,:))>=1 C=[] D=[] for i=1:size(A,1) a=A(i,:)+(B(i,:)-A(i,:))/3 b=A(i,:)+2*(B(i,:)-A(i,:))/3 C=[C A(i,:) b] D=[D a B(i,:)] end A=C B=D p=p+1 )

2013-03-02 09:54:38下载

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zjmohu1
风机模型可以仿真结果,包括风机模型，一部电机模型，变桨距控制方法(Fan control model, the simulation results)

2020-08-27 09:58:13下载

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IEEE-802.11-standarts
matlab code for 802.11a

2013-04-02 15:33:44下载

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AHP
层次分析法的matlab程序，用于构建量化的评价体系等工作(Anaystic Hierarchy Process)

2015-03-03 14:31:37下载

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Elman神经网络预测电力负载
Elman神经网络建立建筑物电力负荷预测模型中遇到的几个关键问题有，数据归一化处理、输入输出样本的选取、隐含层节点数的确定；分别建立Elman神经网络模型，并利用某栋建筑物实际历史电力负载数据进行预测，分析比较与实际数据值的预测精度，得出了一个有效的数据预测模型。(Several key problems encountered in building power load forecasting model based on Elman neural network are data normalization, selection of input and output samples, and determination of the number of hidden layer nodes. Elman neural network models are established respectively, and the actual historical power load data of a building are used for forecasting. Effective data prediction model.)

2019-05-16 19:07:39下载

积分：1

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