matlab神经网络实例(超级简单)
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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ansys workbench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 16.5
15 2.5 6 14.9 16.5 6 14.9 16.5
25 2.5 6 14.9 16.5 6 14.9 16.5
20 1 6 14.9 16.5 6 14.9 16.5
20 4 6 14.9 16.5 6 14.9 16.5
20 2.5 2 14.9 16.5 6 14.9 16.5
20 2.5 10 14.9 16.5 6 14.9 16.5
20 2.5 6 10 16.5 6 14.9 16.5
20 2.5 6 19.8 16.5 6 14.9 16.5
20 2.5 6 14.9 10 6 14.9 16.5
20 2.5 6 14.9 23 6 14.9 16.5
20 2.5 6 14.9 16.5 2 14.9 16.5
20 2.5 6 14.9 16.5 10 14.9 16.5
20 2.5 6 14.9 16.5 6 10 16.5
20 2.5 6 14.9 16.5 6 19.8 16.5
20 2.5 6 14.9 16.5 6 14.9 10
20 2.5 6 14.9 16.5 6 14.9 23
17.51238947 1.75371684 4.009911573 12.46214168 13.26610631 4.009911573
12.46214168 19.73389369
22.48761053 1.75371684 4.009911573 12.46214168 13.26610631 4.009911573
12.46214168 13.26610631
17.51238947 3.24628316 4.009911573 12.46214168 13.26610631 4.009911573
22.48761053 3.24628316 4.009911573 12.46214168 13.26610631 4.009911573
17.33785832 13.26610631
17.51238947 1.75371684 7.990088427 12.46214168 13.26610631 4.009911573
17.33785832 19.73389369
22.48761053 1.75371684 7.990088427 12.46214168 13.26610631 4.009911573
17.33785832 13.26610631
17.51238947 3.24628316 7.990088427 12.46214168 13.26610631 4.009911573
12.46214168 19.73389369
22.48761053 3.24628316 7.990088427 12.46214168 13.26610631 4.009911573
12.46214168 13.26610631
17.51238947 1.75371684 4.009911573 17.33785832 13.26610631 4.009911573
17.33785832 13.26610631
22.48761053 1.75371684 4.009911573 17.33785832 13.26610631 4.009911573
17.33785832 19.73389369
17.51238947 3.24628316 4.009911573 17.33785832 13.26610631 4.009911573
12.46214168 13.26610631
22.48761053 3.24628316 4.009911573 17.33785832 13.26610631 4.009911573
12.46214168 19.73389369
17.51238947 1.75371684 7.990088427 17.33785832 13.26610631 4.009911573
12.46214168 13.26610631
22.48761053 1.75371684 7.990088427 17.33785832 13.26610631 4.009911573
12.46214168 19.73389369
17.51238947 3.24628316 7.990088427 17.33785832 13.26610631 4.009911573
17.33785832 13.26610631
22.48761053 3.24628316 7.990088427 17.33785832 13.26610631 4.009911573
17.33785832 19.73389369
17.51238947 1.75371684 4.009911573 12.46214168 19.73389369 4.009911573
17.33785832 13.26610631
22.48761053 1.75371684 4.009911573 12.46214168 19.73389369 4.009911573
17.33785832 19.73389369
17.51238947 3.24628316 4.009911573 12.46214168 19.73389369 4.009911573
12.46214168 13.26610631
22.48761053 3.24628316 4.009911573 12.46214168 19.73389369 4.009911573
12.46214168 19.73389369
17.51238947 1.75371684 7.990088427 12.46214168 19.73389369 4.009911573
12.46214168 13.26610631
22.48761053 1.75371684 7.990088427 12.46214168 19.73389369 4.009911573
12.46214168 19.73389369
17.51238947 3.24628316 7.990088427 12.46214168 19.73389369 4.009911573
17.33785832 13.26610631
22.48761053 3.24628316 7.990088427 12.46214168 19.73389369 4.009911573
17.33785832 19.73389369
17.51238947 1.75371684 4.009911573 17.33785832 19.73389369 4.009911573