matlab神经网络实例超级简单

1、介绍神经网络算法在机械结构优化中的应用的例子如果看完了还有（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,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方t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

2、如下202.5614.916.5614.916.5152.5614.916.5614.916.5252.5614.916.5614.916.5201614.916.5614.916.5204614.916.5614.916.5202.5214.916.5614.916.5202.51014.916.5614.916.5202.561016.5614.916.5202.5619.816.5614.916.5202.5614.910614.916.5202.5614.923614.916.5202.5614.916.5214.916.5202.5614.916.51014.916.5202.561

3、4.916.561016.5202.5614.916.5619.816.5202.5614.916.5614.910202.5614.916.5614.923P=17.512389471.753716844.00991157312.4621416813.266106314.00991157312.4621416819.7338936922.487610531.753716844.00991157312.4621416813.266106314.00991157312.4621416813.2661063117.512389473.246283164.00991157312.4621416813

4、.266106314.00991157317.3378583219.7338936922.487610533.246283164.00991157317.3378583213.2661063117.512389471.753716847.99008842717.3378583219.7338936922.487610531.753716847.99008842717.3378583213.2661063117.512389473.246283167.99008842712.4621416819.7338936922.487610533.246283167.99008842712.4621416

5、813.2661063117.512389471.753716844.00991157317.3378583213.2661063122.487610531.753716844.00991157317.3378583219.7338936917.512389473.246283164.00991157312.4621416813.2661063122.487610533.246283164.00991157312.4621416819.7338936917.512389471.753716847.99008842712.4621416813.2661063122.487610531.75371

6、6847.99008842712.4621416819.7338936917.512389473.246283167.99008842717.3378583213.2661063122.487610533.246283167.99008842717.3378583219.7338936917.512389471.753716844.00991157317.3378583213.2661063122.487610531.753716844.00991157317.3378583219.7338936917.512389473.246283164.00991157312.4621416813.26

7、61063122.487610533.246283164.00991157312.4621416819.7338936917.512389471.753716847.99008842712.4621416813.2661063122.487610531.753716847.99008842712.4621416819.7338936917.512389473.246283167.99008842717.3378583213.2661063122.487610533.246283167.99008842717.3378583219.7338936917.512389471.753716844.0

8、0991157312.4621416812.4621416812.4621416812.4621416812.4621416817.3378583217.3378583217.3378583217.3378583217.3378583217.3378583217.3378583217.3378583212.4621416812.4621416812.4621416812.4621416812.4621416812.4621416812.4621416812.4621416813.2661063113.2661063113.2661063113.2661063113.2661063113.266

9、1063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063119.7338936919.7338936919.7338936919.7338936919.7338936919.7338936919.7338936919.733893694.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115

10、734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.0099115734.00991157312.4621416819.7338936922.487610531.753716844.00991157317.3378583212.4621416813.2661063117.512389473.246283164.00991157317.3378583217.3378583219.7338936922.487610533.246283164.00991157317.3378583217.3

11、378583213.2661063117.512389471.753716847.99008842717.3378583217.3378583219.7338936922.487610531.753716847.99008842717.3378583217.3378583213.2661063117.512389473.246283167.99008842717.3378583212.4621416819.7338936922.487610533.246283167.99008842717.3378583219.733893694.00991157319.733893694.009911573

12、19.733893694.00991157319.733893694.00991157319.733893694.00991157319.733893694.00991157319.733893694.00991157312.4621416813.2661063117.512389471.753716844.00991157312.4621416817.3378583213.2661063122.487610531.753716844.00991157312.4621416817.3378583219.7338936917.512389473.246283164.00991157312.462

13、1416812.4621416813.2661063122.487610533.246283164.00991157312.4621416812.4621416819.7338936917.512389471.753716847.99008842712.4621416812.4621416813.2661063122.487610531.753716847.99008842712.4621416812.4621416819.7338936917.512389473.246283167.99008842712.4621416817.3378583213.2661063122.487610533.

14、246283167.99008842712.4621416817.3378583219.7338936917.512389471.753716844.00991157317.3378583212.4621416819.7338936922.487610531.753716844.00991157317.3378583212.4621416813.2661063117.512389473.246283164.00991157317.3378583217.3378583219.7338936922.487610533.246283164.00991157317.3378583217.3378583

15、213.2661063117.512389471.753716847.99008842717.3378583217.3378583219.7338936922.487610531.753716847.99008842717.3378583213.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.2661063113.266106317.9900884277.9900884277.99

16、00884277.9900884277.9900884277.9900884277.9900884277.9900884277.9900884277.9900884277.9900884277.9900884277.9900884277.99008842717.3378583213.2661063117.512389473.246283167.99008842717.3378583213.266106317.99008842717.3378583213.2661063112.4621416813.2661063117.3378583213.2661063113.266106317.990088

17、42719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.733893697.99008842719.73389369

18、7.99008842719.733893697.99008842719.733893697.99008842712.4621416819.7338936922.487610533.246283167.99008842717.3378583212.4621416813.2661063117.512389471.753716844.00991157312.4621416812.4621416819.7338936922.487610531.753716844.00991157312.4621416812.4621416813.2661063117.512389473.246283164.00991

19、157312.4621416817.3378583219.7338936922.487610533.246283164.00991157312.4621416817.512389471.753716847.99008842712.4621416817.3378583219.7338936922.487610531.753716847.99008842712.4621416817.3378583213.2661063117.512389473.246283167.99008842712.4621416812.4621416819.7338936922.487610533.246283167.99

20、008842712.4621416817.512389471.753716844.00991157317.3378583217.3378583213.2661063122.487610531.753716844.00991157317.3378583217.3378583219.7338936917.512389473.246283164.00991157317.3378583212.4621416813.2661063122.487610533.246283164.00991157317.3378583212.4621416819.7338936917.512389471.753716847

21、.99008842717.3378583212.4621416813.2661063122.487610531.753716847.99008842717.3378583212.4621416819.7338936917.512389473.246283167.99008842717.3378583222.487610533.246283167.99008842717.3378583217.3378583219.73389369'；%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），

22、对应的下面的输出T的是3x81的矩阵。T=150.7492.2849913.466165.1482.640219.6525138.0611.9297617.2795149.4462.2570413.766151.6422.3129313.166147.1462.2294714.062154.1312.340512.87144.1642.257613.76155.8892.3123713.172150.6462.2849913.466150.6212.2849913.466147.0912.2294714.062154.1662.340512.87144.2892.257613.76155.55

23、32.3123713.172150.6532.2849913.466150.7042.2849913.466148.4242.3760912.4879134.9522.0191716.3197154.2642.4186512.0311141.2072.0686415.7885156.4922.4405111.7964142.6712.0835815.6282152.4732.4466411.7306138.3292.0966315.488159.6962.4125212.0969145.9472.0555915.9287155.4012.4186512.0311141.732.0686415.

24、7885157.4082.4585811.6024144.12.1016615.4341163.4832.5011411.1455150.4832.1511414.9029154.1112.394312.2924140.4182.0373816.1242149.2532.4004412.2266135.9972.0504315.984151.5182.422311.9919137.2572.0653715.8237158.052.4648511.535143.7392.1148515.2925153.6412.394312.2924140.7232.0373816.1242158.9562.4

25、368611.8355146.9332.0868515.593160.7312.476811.4068149.3152.1198715.2386156.8422.4829311.341145.172.1329215.0984156.9422.4585811.6024143.9482.1016615.4341152.5032.4466411.7306138.4862.0966315.488154.842.468511.4959139.7952.1115715.3276161.5742.5291410.845147.5022.1791314.6024156.9752.4405111.7964143

26、.062.0835815.6282162.6882.5011411.1455150.4832.1511414.9029164.5882.5410810.7168153.0242.1841514.5485160.9082.5291410.845147.7942.1791314.6024151.4372.422311.9919137.3862.0653715.8237156.9792.4829311.341144.9152.1329215.0984159.1672.5047911.1063146.2292.1478614.9381155.6992.4928511.2345140.7672.1428

27、414.992161.7822.476811.4068149.1242.1198715.2386157.8192.4648511.535143.82.1148515.2925159.5532.5047911.1063146.1862.1478614.9381166.5122.5654210.4554153.8962.2154214.2129'；%T为目标矢量PPps=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络，隐层神经元为15输出层神经元为3ne