我一直在学习神经网络，并试图利用MATLAB的神经网络工具箱和Mathworks提供的示例。我发现这个主题到目前为止非常有趣。
我对MATLAB代码有基本的了解。然而，我在理解如何在神经网络训练完成后输入自己的测试数据方面遇到了困难。Mathworks的所有示例似乎在网络训练完成后就结束了。
例如： http://in.mathworks.com/help/nnet/examples/wine-classification.html#zmw57dd0e324
在上面的链接中有一个“测试神经网络”的部分，但没有提供关于所使用的测试数据的信息。
我知道这可能是一个新手问题，但我很感激任何能帮助我理解这一点的帮助。
回答：
您可以使用神经网络工具箱中的sim命令。基本操作是提交一个M x N矩阵，其中M是特征总数，N是样本总数。M与您拥有的输入层神经元总数相关。因此，对于每个输入，您需要在矩阵中提供一列数据。
因此，一旦您训练了网络，并且如果您遵循示例，您的神经网络应该存储在net中。只要创建您的输入数据，并使用您期望的输入“模拟”神经网络即可。
因此，如果您的输入数据存储在X中，您可以这样做：
Y = sim(net, X);然而，如果您想查看神经网络在相同输入下的表现，可以尝试使用变量x，因为在帖子中提到的数据集中，输入数据存储在x中：
Y = sim(net, x);或者，net是一个可调用的对象。您可以通过直接使用net对象来实现与sim相同的效果：
Y = net(x);如果您想进行一个小测试，可以尝试加载房价数据集：
[x,t] = house_dataset;这将返回一个包含13个特征（行）的数据矩阵，共有506个样本。这些数据存储在矩阵x中。向量t是输出层的值，或者是通过神经网络运行后的目标值。您可以使用一个具有10个神经元的隐藏层的网络来训练这个数据集的神经网络，然后我们可以看到神经网络在相同输入数据下的表现如何：
net = feedforwardnet(10);net = train(net, x, t); %// 打开神经网络训练工具并训练y = sim(net, x); %// 或者 y = net(x);然后，您可以并排显示结果并进行比较
disp([y; t]);顶行是预测值，底行是真实值：
Columns 1 through 7 23.1000 22.5739 34.6595 32.9133 33.3979 24.0131 19.6418 24.0000 21.6000 34.7000 33.4000 36.2000 28.7000 22.9000 Columns 8 through 14 19.3123 15.3914 18.8765 19.6154 18.8690 20.0817 18.0593 27.1000 16.5000 18.9000 15.0000 18.9000 21.7000 20.4000 Columns 15 through 21 17.4043 17.7989 19.4390 17.8489 18.5007 17.3547 14.3393 18.2000 19.9000 23.1000 17.5000 20.2000 18.2000 13.6000 Columns 22 through 28 16.9866 17.5983 15.2679 16.3294 13.2075 15.6425 14.3992 19.6000 15.2000 14.5000 15.6000 13.9000 16.6000 14.8000 Columns 29 through 35 18.8453 20.4586 14.5208 15.4776 12.6204 14.5306 12.2213 18.4000 21.0000 12.7000 14.5000 13.2000 13.1000 13.5000 Columns 36 through 42 21.5945 21.7292 21.3951 22.8980 29.5480 35.4459 33.3658 18.9000 20.0000 21.0000 24.7000 30.8000 34.9000 26.6000 Columns 43 through 49 24.9201 25.2191 22.0473 20.2307 21.9648 19.1073 16.4167 25.3000 24.7000 21.2000 19.3000 20.0000 16.6000 14.4000 Columns 50 through 56 19.1333 19.4673 19.6465 26.6304 20.6860 18.0080 34.7084 19.4000 19.7000 20.5000 25.0000 23.4000 18.9000 35.4000 Columns 57 through 63 23.9052 28.8725 23.2335 21.1107 18.5425 17.7766 22.5957 24.7000 31.6000 23.3000 19.6000 18.7000 16.0000 22.2000 Columns 64 through 70 24.6137 30.3093 24.5964 20.3996 21.0408 18.2597 19.9876 25.0000 33.0000 23.5000 19.4000 22.0000 17.4000 20.9000 Columns 71 through 77 25.4133 21.4175 22.4705 24.1181 25.2216 22.2482 20.4357 24.2000 21.7000 22.8000 23.4000 24.1000 21.4000 20.0000 Columns 78 through 84 21.7641 20.8850 20.6352 27.3050 22.6721 23.7894 21.7782 20.8000 21.2000 20.3000 28.0000 23.9000 24.8000 22.9000 Columns 85 through 91 24.1746 26.2748 22.9204 23.2649 27.7500 30.4014 23.7201 23.9000 26.6000 22.5000 22.2000 23.6000 28.7000 22.6000 Columns 92 through 98 23.1654 24.7010 25.0065 20.9675 26.6610 22.2313 41.1054 22.0000 22.9000 25.0000 20.6000 28.4000 21.4000 38.7000 Columns 99 through 105 43.3199 33.8214 22.5828 23.9000 19.4996 19.0978 19.2686 43.8000 33.2000 27.5000 26.5000 18.6000 19.3000 20.1000 Columns 106 through 112 17.6835 18.1297 19.9324 19.4827 18.8635 22.3501 23.0958 19.5000 19.5000 20.4000 19.8000 19.4000 21.7000 22.8000 Columns 113 through 119 19.2355 19.4148 21.4072 18.6230 21.0624 19.4977 19.8504 18.8000 18.7000 18.5000 18.3000 21.2000 19.2000 20.4000 Columns 120 through 126 19.7301 22.3515 21.7456 19.6909 17.3642 18.6674 21.4590 19.3000 22.0000 20.3000 20.5000 17.3000 18.8000 21.4000 Columns 127 through 133 15.7843 14.9932 19.6013 14.6810 19.5595 18.3563 18.6026 15.7000 16.2000 18.0000 14.3000 19.2000 19.6000 23.0000 Columns 134 through 140 15.2750 11.8104 18.7860 16.7552 19.7692 15.9850 17.8815 18.4000 15.6000 18.1000 17.4000 17.1000 13.3000 17.8000 Columns 141 through 147 18.1044 11.2998 13.7603 16.7915 14.2739 15.8247 12.1940 14.0000 14.4000 13.4000 15.6000 11.8000 13.8000 15.6000 Columns 148 through 154 14.8796 15.9726 17.0902 20.0606 12.5238 15.0568 15.3508 14.6000 17.8000 15.4000 21.5000 19.6000 15.3000 19.4000 Columns 155 through 161 16.8791 17.1680 7.5081 38.1429 27.6726 23.4515 33.1939 17.0000 15.6000 13.1000 41.3000 24.3000 23.3000 27.0000 Columns 162 through 168 49.9039 49.8868 49.4694 20.0338 20.5124 51.1646 19.0164 50.0000 50.0000 50.0000 22.7000 25.0000 50.0000 23.8000 Columns 169 through 175 23.1614 23.9231 18.1501 19.0578 24.0764 25.6909 24.2217 23.8000 22.3000 17.4000 19.1000 23.1000 23.6000 22.6000 Columns 176 through 182 29.0459 23.7307 24.3887 28.9583 36.7788 41.2092 29.0054 29.4000 23.2000 24.6000 29.9000 37.2000 39.8000 36.2000 Columns 183 through 189 36.1596 29.3311 24.9179 26.8610 46.6416 30.5403 29.2822 37.9000 32.5000 26.4000 29.6000 50.0000 32.0000 29.8000 Columns 190 through 196 32.3537 30.5739 28.0238 33.5408 32.5201 30.3314 47.7960 34.9000 37.0000 30.5000 36.4000 31.1000 29.1000 50.0000 Columns 197 through 203 35.5760 29.9340 32.4614 23.7173 24.6185 22.3271 40.2040 33.3000 30.3000 34.6000 34.9000 32.9000 24.1000 42.3000 Columns 204 through 210 48.7640 51.9776 21.6903 23.1472 19.7713 20.8484 19.3971 48.5000 50.0000 22.6000 24.4000 22.5000 24.4000 20.0000 Columns 211 through 217 19.2595 20.7310 21.9656 24.4268 23.4818 22.6366 23.3319 21.7000 19.3000 22.4000 28.1000 23.7000 25.0000 23.3000 Columns 218 through 224 27.5792 19.9003 21.9243 27.6622 25.8529 27.0425 26.9247 28.7000 21.5000 23.0000 26.7000 21.7000 27.5000 30.1000 Columns 225 through 231 43.9926 46.3891 41.9625 31.2884 42.9222 31.0752 24.0971 44.8000 50.0000 37.6000 31.6000 46.7000 31.5000 24.3000 Columns 232 through 238 33.9499 45.8143 44.0646 27.7080 24.5834 24.8484 33.5038 31.7000 41.7000 48.3000 29.0000 24.0000 25.1000 31.5000 Columns 239 through 245 27.1918 25.1814 24.1256 18.9528 20.8431 27.5930 15.9916 23.7000 23.3000 22.0000 20.1000 22.2000 23.7000 17.6000 Columns 246 through 252 15.4457 21.3048 18.9629 22.2684 26.9249 26.6310 28.3915 18.5000 24.3000 20.5000 24.5000 26.2000 24.4000 24.8000 Columns 253 through 259 31.1707 42.3749 21.8852 20.0264 41.3473 51.4709 37.5230 29.6000 42.8000 21.9000 20.9000 44.0000 50.0000 36.0000 Columns 260 through 266 31.7275 35.0836 40.2452 48.8115 34.8549 36.0551 21.6751 30.1000 33.8000 43.1000 48.8000 31.0000 36.5000 22.8000 Columns 267 through 273 30.0784 48.2556 43.5570 21.8858 19.8884 23.6277 24.2718 30.7000 50.0000 43.5000 20.7000 21.1000 25.2000 24.4000 Columns 274 through 280 36.2902 33.7512 31.2525 32.4099 33.0711 26.3885 36.7014 35.2000 32.4000 32.0000 33.2000 33.1000 29.1000 35.1000 Columns 281 through 287 45.9504 36.7908 46.8473 51.1511 31.4501 23.5448 21.8759 45.4000 35.4000 46.0000 50.0000 32.2000 22.0000 20.1000 Columns 288 through 294 23.4287 22.6450 25.2664 32.6864 35.8508 30.2136 23.6842 23.2000 22.3000 24.8000 28.5000 37.3000 27.9000 23.9000 Columns 295 through 301 21.9361 29.8485 27.2524 20.1174 23.8767 30.0819 26.0127 21.7000 28.6000 27.1000 20.3000 22.5000 29.0000 24.8000 Columns 302 through 308 24.2473 26.5010 32.4189 34.7875 28.9162 35.5226 30.2009 22.0000 26.4000 33.1000 36.1000 28.4000 33.4000 28.2000 Columns 309 through 315 24.1533 19.4709 18.2852 22.0597 19.2040 20.6719 22.5350 22.8000 20.3000 16.1000 22.1000 19.4000 21.6000 23.8000 Columns 316 through 322 17.3670 19.0604 19.3382 22.5443 21.3041 23.1810 22.7061 16.2000 17.8000 19.8000 23.1000 21.0000 23.8000 23.1000 Columns 323 through 329 20.5965 18.3056 23.5467 24.9056 22.9716 21.5595 21.3041 20.4000 18.5000 25.0000 24.6000 23.0000 22.2000 19.3000 Columns 330 through 336 25.0149 21.6011 16.6778 20.7220 23.1595 23.1583 21.2709 22.6000 19.8000 17.1000 19.4000 22.2000 20.7000 21.1000 Columns 337 through 343 20.5881 20.0957 22.1791 21.6646 20.9052 32.2760 19.6094 19.5000 18.5000 20.6000 19.0000 18.7000 32.7000 16.5000 Columns 344 through 350 26.6120 30.8497 18.2102 17.3026 24.0279 27.7069 28.6764 23.9000 31.2000 17.5000 17.2000 23.1000 24.5000 26.6000 Columns 351 through 357 24.0160 22.8876 19.0665 27.5481 18.8396 20.1889 17.1304 22.9000 24.1000 18.6000 30.1000 18.2000 20.6000 17.8000 Columns 358 through 364 19.3401 22.0580 20.7871 25.3794 19.9338 23.0456 20.1196 21.7000 22.7000 22.6000 25.0000 19.9000 20.8000 16.8000 Columns 365 through 371 23.6761 32.1277 24.0001 21.9790 51.1021 48.6596 51.0467 21.9000 27.5000 21.9000 23.1000 50.0000 50.0000 50.0000 Columns 372 through 378 38.7150 40.0930 11.7337 11.7271 19.3180 12.2218 14.0871 50.0000 50.0000 13.8000 13.8000 15.0000 13.9000 13.3000 Columns 379 through 385 10.6245 12.3102 11.2811 12.5886 12.6262 12.7699 8.7457 13.1000 10.2000 10.4000 10.9000 11.3000 12.3000 8.8000 Columns 386 through 392 8.8422 5.2698 8.0072 8.1700 14.5322 17.5716 16.0186 7.2000 10.5000 7.4000 10.2000 11.5000 15.1000 23.2000 Columns 393 through 399 9.8385 19.5303 15.8293 16.7939 16.1594 16.0031 5.2560 9.7000 13.8000 12.7000 13.1000 12.5000 8.5000 5.0000 Columns 400 through 406 11.0778 7.5946 12.6534 13.7895 7.5276 6.5614 4.2802 6.3000 5.6000 7.2000 12.1000 8.3000 8.5000 5.0000 Columns 407 through 413 13.7780 28.4038 16.2167 15.3346 16.2930 10.6613 11.2681 11.9000 27.9000 17.2000 27.5000 15.0000 17.2000 17.9000 Columns 414 through 420 15.1122 7.6163 7.5657 8.3258 10.5912 8.7514 10.9284 16.3000 7.0000 7.2000 7.5000 10.4000 8.8000 8.4000 Columns 421 through 427 16.8908 18.9752 21.6162 9.5188 12.4425 8.3453 14.5831 16.7000 14.2000 20.8000 13.4000 11.7000 8.3000 10.2000 Columns 428 through 434 9.2716 13.8301 9.5708 14.5073 11.7783 19.1158 16.5270 10.9000 11.0000 9.5000 14.5000 14.1000 16.1000 14.3000 Columns 435 through 441 14.5899 9.5630 11.7702 7.7339 5.5916 11.7561 6.6963 11.7000 13.4000 9.6000 8.7000 8.4000 12.8000 10.5000 Columns 442 through 448 12.4933 17.3840 12.9751 10.0512 9.2991 15.4655 14.4212 17.1000 18.4000 15.4000 10.8000 11.8000 14.9000 12.6000 Columns 449 through 455 13.2374 13.2950 11.7887 14.2879 15.7591 12.5250 10.9566 14.1000 13.0000 13.4000 15.2000 16.1000 17.8000 14.9000 Columns 456 through 462 13.5464 11.8807 12.4477 16.1538 16.4501 16.3458 18.2755 14.1000 12.7000 13.5000 14.9000 20.0000 16.4000 17.7000 Columns 463 through 469 17.0225 20.7452 18.1601 20.7623 12.4873 15.6683 15.4485 19.5000 20.2000 21.4000 19.9000 19.0000 19.1000 19.1000 Columns 470 through 476 19.0019 17.4639 22.5748 20.2223 22.6186 17.8242 13.9765 20.1000 19.9000 19.6000 23.2000 29.8000 13.8000 13.3000 Columns 477 through 483 16.1047 11.9184 16.2553 22.4462 23.2027 26.7181 26.4324 16.7000 12.0000 14.6000 21.4000 23.0000 23.7000 25.0000 Columns 484 through 490 21.5557 20.0931 20.0937 16.1954 23.1288 15.7670 12.6886 21.8000 20.6000 21.2000 19.1000 20.6000 15.2000 7.0000 Columns 491 through 497 8.1229 18.0084 20.7898 20.3537 22.0706 21.7872 19.0696 8.1000 13.6000 20.1000 21.8000 24.5000 23.1000 19.7000 Columns 498 through 504 19.4128 21.0991 18.7917 20.1158 22.0507 17.6495 24.3503 18.3000 21.2000 17.5000 16.8000 22.4000 20.6000 23.9000 Columns 505 through 506 22.1611 16.1823 22.0000 11.9000您可以看到，大多数样本的匹配度较高，并且相对接近。然而，一些输出相差甚远……所以现在关键在于调整神经网络，但不要调整过度，否则会导致数据过拟合。
有关sim的更多详细信息，请查看文档： http://www.mathworks.com/help/nnet/ref/sim.html
