matlab神经网络实例(超级简单)

介绍神经网络算法在机械结构优化中的应用的例子（大家要学习的时候只需要把输入输出变量更改为你自己的数据既可以了，如果看完了还有问题的话可以加我微博“极南师兄”给我留言，与大家共同进步)。把一个结构的8个尺寸参数设计为变量，如上图所示，对应的质量，温差，面积作为输出。用神经网络拟合变量与输出的数学模型，首相必须要有数据来源，这里我用复合中心设计法则构造设计点，根据规则，八个变量将构造出81个设计点。然后在ansysworkbench中进行81次仿真（先在proe建模并设置变量，将模型导入wokbench中进行相应的设置，那么就会自动的完成81次仿真，将结果导出来exceel文件）Matlab程序如下P=[20 2.5 6 14.9 16.5 6 14.9 16.515 2.5 6 14.9 16.5 6 14.9 16.525 2.5 6 14.9 16.5 6 14.9 16.520 1 6 14.9 16.5 6 14.916.520 4 6 14.9 16.5 6 14.9 16.520 2.5 2 14.916.5 6 14.9 16.520 2.5 10 14.9 16.5 6 14.9 16.520 2.5 6 10 16.5 6 14.9 16.520 2.5 6 19.8 16.5 6 14.9 16.520 2.5 6 14.9 10 6 14.9 16.520 2.5 6 14.9 23 6 14.9 16.520 2.5 6 14.9 16.5 2 14.9 16.520 2.5 6 14.9 16.5 10 14.9 16.520 2.5 6 14.9 16.5 6 10 16.520 2.5 6 14.9 16.5 6 19.8 16.520 2.5 6 14.9 16.5 6 14.9 1020 2.5 6 14.9 16.5 6 14.9 2317.51238947 1.75371684 4.009911573 12.46214168 13.26610631 4.009911573 12.46214168 19.7338936922.48761053 1.75371684 4.009911573 12.46214168 13.26610631 4.009911573 12.46214168 13.2661063117.51238947 3.24628316 4.009911573 12.46214168 13.26610631 4.009911573 17.33785832 19.7338936922.48761053 3.24628316 4.009911573 12.46214168 13.26610631 4.009911573 17.33785832 13.2661063117.51238947 1.75371684 7.990088427 12.46214168 13.26610631 4.009911573 17.33785832 19.7338936922.48761053 1.75371684 7.990088427 12.46214168 13.26610631 4.009911573 17.33785832 13.2661063117.51238947 3.24628316 7.990088427 12.46214168 13.26610631 4.009911573 12.46214168 19.7338936922.48761053 3.24628316 7.990088427 12.46214168 13.26610631 4.009911573 12.46214168 13.2661063117.51238947 1.75371684 4.009911573 17.33785832 13.26610631 4.009911573 17.33785832 13.2661063122.48761053 1.75371684 4.009911573 17.33785832 13.26610631 4.009911573 17.33785832 19.7338936917.51238947 3.24628316 4.009911573 17.33785832 13.26610631 4.009911573 12.46214168 13.2661063122.48761053 3.24628316 4.009911573 17.33785832 13.26610631 4.009911573 12.46214168 19.7338936917.51238947 1.75371684 7.990088427 17.33785832 13.26610631 4.009911573 12.46214168 13.2661063122.48761053 1.75371684 7.990088427 17.33785832 13.26610631 4.009911573 12.46214168 19.7338936917.51238947 3.24628316 7.990088427 17.33785832 13.26610631 4.009911573 17.33785832 13.2661063122.48761053 3.24628316 7.990088427 17.33785832 13.26610631 4.009911573 17.33785832 19.7338936917.51238947 1.75371684 4.009911573 12.46214168 19.73389369 4.009911573 17.33785832 13.2661063122.48761053 1.75371684 4.009911573 12.46214168 19.73389369 4.009911573 17.33785832 19.7338936917.51238947 3.24628316 4.009911573 12.46214168 19.73389369 4.009911573 12.46214168 13.2661063122.48761053 3.24628316 4.009911573 12.46214168 19.73389369 4.009911573 12.46214168 19.7338936917.51238947 1.75371684 7.990088427 12.46214168 19.73389369 4.009911573 12.46214168 13.2661063122.48761053 1.75371684 7.990088427 12.46214168 19.73389369 4.009911573 12.46214168 19.7338936917.51238947 3.24628316 7.990088427 12.46214168 19.73389369 4.009911573 17.33785832 13.2661063122.48761053 3.24628316 7.990088427 12.46214168 19.73389369 4.009911573 17.33785832 19.7338936917.51238947 1.75371684 4.009911573 17.33785832 19.73389369 4.009911573 12.46214168 19.7338936922.48761053 1.75371684 4.009911573 17.33785832 19.73389369 4.009911573 12.46214168 13.2661063117.51238947 3.24628316 4.009911573 17.33785832 19.73389369 4.009911573 17.33785832 19.7338936922.48761053 3.24628316 4.009911573 17.33785832 19.73389369 4.009911573 17.33785832 13.2661063117.51238947 1.75371684 7.990088427 17.33785832 19.73389369 4.009911573 17.33785832 19.7338936922.48761053 1.75371684 7.990088427 17.33785832 19.73389369 4.009911573 17.33785832 13.2661063117.51238947 3.24628316 7.990088427 17.33785832 19.73389369 4.009911573 12.46214168 19.7338936922.48761053 3.24628316 7.990088427 17.33785832 19.73389369 4.009911573 12.46214168 13.2661063117.51238947 1.75371684 4.009911573 12.46214168 13.26610631 7.990088427 17.33785832 13.2661063122.48761053 1.75371684 4.009911573 12.46214168 13.26610631 7.990088427 17.33785832 19.7338936917.51238947 3.24628316 4.009911573 12.46214168 13.26610631 7.990088427 12.46214168 13.2661063122.48761053 3.24628316 4.009911573 12.46214168 13.26610631 7.990088427 12.46214168 19.7338936917.51238947 1.75371684 7.990088427 12.46214168 13.26610631 7.990088427 12.46214168 13.2661063122.48761053 1.75371684 7.990088427 12.46214168 13.26610631 7.990088427 12.46214168 19.7338936917.51238947 3.24628316 7.990088427 12.46214168 13.26610631 7.990088427 17.33785832 13.2661063122.48761053 3.24628316 7.990088427 12.46214168 13.26610631 7.990088427 17.33785832 19.7338936917.51238947 1.75371684 4.009911573 17.33785832 13.26610631 7.990088427 12.46214168 19.7338936922.48761053 1.75371684 4.009911573 17.33785832 13.26610631 7.990088427 12.46214168 13.2661063117.51238947 3.24628316 4.009911573 17.33785832 13.26610631 7.990088427 17.33785832 19.7338936922.48761053 3.24628316 4.009911573 17.33785832 13.26610631 7.990088427 17.33785832 13.2661063117.51238947 1.75371684 7.990088427 17.33785832 13.26610631 7.990088427 17.33785832 19.7338936922.48761053 1.75371684 7.990088427 17.33785832 13.26610631 7.990088427 17.33785832 13.2661063117.51238947 3.24628316 7.990088427 17.33785832 13.26610631 7.990088427 12.46214168 19.7338936922.48761053 3.24628316 7.990088427 17.33785832 13.26610631 7.990088427 12.46214168 13.2661063117.51238947 1.75371684 4.009911573 12.46214168 19.73389369 7.990088427 12.46214168 19.7338936922.48761053 1.75371684 4.009911573 12.46214168 19.73389369 7.990088427 12.46214168 13.2661063117.51238947 3.24628316 4.009911573 12.46214168 19.73389369 7.990088427 17.33785832 19.7338936922.48761053 3.24628316 4.009911573 12.46214168 19.73389369 7.990088427 17.33785832 13.2661063117.51238947 1.75371684 7.990088427 12.46214168 19.73389369 7.990088427 17.33785832 19.7338936922.48761053 1.75371684 7.990088427 12.46214168 19.73389369 7.990088427 17.33785832 13.2661063117.51238947 3.24628316 7.990088427 12.46214168 19.73389369 7.990088427 12.46214168 19.7338936922.48761053 3.24628316 7.990088427 12.46214168 19.73389369 7.990088427 12.46214168 13.2661063117.51238947 1.75371684 4.009911573 17.33785832 19.73389369 7.990088427 17.33785832 13.2661063122.48761053 1.75371684 4.009911573 17.33785832 19.73389369 7.990088427 17.33785832 19.7338936917.51238947 3.24628316 4.009911573 17.33785832 19.73389369 7.990088427 12.46214168 13.2661063122.48761053 3.24628316 4.009911573 17.33785832 19.73389369 7.990088427 12.46214168 19.7338936917.51238947 1.75371684 7.990088427 17.33785832 19.73389369 7.990088427 12.46214168 13.2661063122.48761053 1.75371684 7.990088427 17.33785832 19.73389369 7.990088427 12.46214168 19.7338936917.51238947 3.24628316 7.990088427 17.33785832 19.73389369 7.990088427 17.33785832 13.2661063122.48761053 3.24628316 7.990088427 17.33785832 19.73389369 7.990088427 17.33785832 19.73389369]';%注意因为本人做了81组仿真试验，这里的矩阵后面有转置符号，在神经网络模型中，输入P的是8X81的矩阵（把程序复制过来之后格式没对齐，大家自己调整一下啦），对应的下面的输出T的是3x81的矩阵。T=[150.749 2.28499 13.466165.148 2.64021 9.6525138.061 1.92976 17.2795149.446 2.25704 13.766151.642 2.31293 13.166147.146 2.22947 14.062154.131 2.3405 12.87144.164 2.2576 13.76155.889 2.31237 13.172150.646 2.28499 13.466150.621 2.28499 13.466147.091 2.22947 14.062154.166 2.3405 12.87144.289 2.2576 13.76155.553 2.31237 13.172150.653 2.28499 13.466150.704 2.28499 13.466148.424 2.37609 12.4879134.952 2.01917 16.3197154.264 2.41865 12.0311141.207 2.06864 15.7885156.492 2.44051 11.7964142.671 2.08358 15.6282152.473 2.44664 11.7306138.329 2.09663 15.488159.696 2.41252 12.0969145.947 2.05559 15.9287155.401 2.41865 12.0311141.73 2.06864 15.7885157.408 2.45858 11.6024144.1 2.10166 15.4341163.483 2.50114 11.1455150.483 2.15114 14.9029154.111 2.3943 12.2924140.418 2.03738 16.1242149.253 2.40044 12.2266135.997 2.05043 15.984151.518 2.4223 11.9919137.257 2.06537 15.8237158.05 2.46485 11.535143.739 2.11485 15.2925153.641 2.3943 12.2924140.723 2.03738 16.1242158.956 2.43686 11.8355146.933 2.08685 15.593160.731 2.4768 11.4068149.315 2.11987 15.2386156.842 2.48293 11.341145.17 2.13292 15.0984156.942 2.45858 11.6024143.948 2.10166 15.4341152.503 2.44664 11.7306138.486 2.09663 15.488154.84 2.4685 11.4959139.795 2.11157 15.3276161.574 2.52914 10.845147.502 2.17913 14.6024156.975 2.44051 11.7964143.06 2.08358 15.6282162.688 2.50114 11.1455150.483 2.15114 14.9029164.588 2.54108 10.7168153.024 2.18415 14.5485160.908 2.52914 10.845147.794 2.17913 14.6024151.437 2.4223 11.9919137.386 2.06537 15.8237156.979 2.48293 11.341144.915 2.13292 15.0984159.167 2.50479 11.1063146.229 2.14786 14.9381155.699 2.49285 11.2345140.767 2.14284 14.992161.782 2.4768 11.4068149.124 2.11987 15.2386157.819 2.46485 11.535143.8 2.11485 15.2925159.553 2.50479 11.1063146.186 2.14786 14.9381166.512 2.56542 10.4554153.896 2.21542 14.2129]';%T为目标矢量[PP,ps]=mapminmax(P,-1,1);%把P归一化处理变为pp，在范围（-1,1）内%把T归一化处理变TT，在范围（-1,1）内，归一化主要是为了消除不通量岗对结果的影响[TT,ps]=mapminmax(T,-1,1);%创建三层前向神经网