matlab神經(jīng)網(wǎng)絡(luò)實(shí)例(超級簡單)

介紹神經(jīng)網(wǎng)絡(luò)算法在機(jī)械結(jié)構(gòu)優(yōu)化中的應(yīng)用的例子（大家要學(xué)習(xí)的時候只需要把輸入輸出變量更改為你自己的數(shù)據(jù)既可以了，如果看完了還有問題的話可以加我微博“極南師兄”給我留言，與大家共同進(jìn)步)。把一個結(jié)構(gòu)的8個尺寸參數(shù)設(shè)計(jì)為變量，如上圖所示，對應(yīng)的質(zhì)量，溫差，面積作為輸出。用神經(jīng)網(wǎng)絡(luò)擬合變量與輸出的數(shù)學(xué)模型，首相必須要有數(shù)據(jù)來源，這里我用復(fù)合中心設(shè)計(jì)法則構(gòu)造設(shè)計(jì)點(diǎn)，根據(jù)規(guī)則，八個變量將構(gòu)造出81個設(shè)計(jì)點(diǎn)。然后在ansysworkbench中進(jìn)行81次仿真（先在proe建模并設(shè)置變量，將模型導(dǎo)入wokbench中進(jìn)行相應(yīng)的設(shè)置，那么就會自動的完成81次仿真，將結(jié)果導(dǎo)出來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?yàn)楸救俗隽?1組仿真試驗(yàn)，這里的矩陣后面有轉(zhuǎn)置符號，在神經(jīng)網(wǎng)絡(luò)模型中，輸入P的是8X81的矩陣（把程序復(fù)制過來之后格式?jīng)]對齊，大家自己調(diào)整一下啦），對應(yīng)的下面的輸出T的是3x81的矩陣。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為目標(biāo)矢量[PP,ps]=mapminmax(P,-1,1);%把P歸一化處理變?yōu)閜p，在范圍（-1,1）內(nèi)%把T歸一化處理變TT，在范圍（-1,1）內(nèi)，歸一化主要是為了消除不通量崗對結(jié)果的影響[TT,ps]=mapminmax(T,-1,1);%創(chuàng)建三層前向神經(jīng)網(wǎng)