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Description: BP人工神经网络训练源码，采用三层网络结构，固定节点数目，可以设定学习速度和学习次数。
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Size: 29069 |
Author: gabriel |
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Description: 基于神经网络工具箱函数trainbp和simuff实现的BP算法源程序，内附样本数据和测试数据。-based on neural network toolbox function and simuff trainbp BP algorithm to achieve the source, containing sample data and test data.
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Size: 555 |
Author: 萧雪鱼 |
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Description: 基于神经网络工具箱函数trainbp和simuff实现的BP算法源程序，内附样本数据和测试数据。-based on neural network toolbox function and simuff trainbp BP algorithm to achieve the source, containing sample data and test data.
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Size: 1024 |
Author: 萧雪鱼 |
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Description: BP人工神经网络训练源码，采用三层网络结构，固定节点数目，可以设定学习速度和学习次数。-BP artificial neural network training source, using the three-tier network architecture, a fixed number of nodes, you can set the number of learning speed and learning.
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Size: 28672 |
Author: gabriel |
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Description: 人工神经网络系统的训练 TRAIN
BP算法存在局部极小点，收敛速度慢等缺点，改进的BP算法。-Artificial neural network training algorithm TRAINBP local minimum points, such as the shortcomings of slow convergence, improved BP algorithm.
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Size: 2048 |
Author: q |
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Description: 人脸识别过程中的奇异值分解算法代码，亲测可用，实现步骤为：
feature = allFeature(1) //featurenumber=8,16,24,32,48,64,80
[pn,pnewn,t,num_train,num_test] = train_test(feature,num_train) //num_train=1~10
[net] = createBP(pn) //110,tansig,purelin,trainrp,1e-5,8000,0.005
[net,tr] = trainBP(net,pn,t)
[result_test,result_train,count_test,count_train,Test_reg,Train_reg,Total_reg] = result(net,pnewn,pn,num_train,num_test) -Recognition process singular value decomposition algorithm code, pro-test can be used to achieve the steps of: feature = allFeature (1) //featurenumber = 8,16,24,32,48,64,80 [pn, pnewn, t , num_train, num_test] = train_test (feature, num_train) //num_train = 1 ~ 10 [net] = createBP (pn) // 110, tansig, purelin, trainrp, 1e-5, 8000,0.005 [net, tr ] = trainBP (net, pn, t) [result_test, result_train, count_test, count_train, Test_reg, Train_reg, Total_reg] = result (net, pnewn, pn, num_train, num_test)
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Size: 8075264 |
Author: 陈伟 |
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