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ÏëÓÃ1stopt×öÒ»¸öÔ¼ÊøÉñ¾ÍøÂ磬´ó¸ÅÒâ˼ÊÇ Predicted = ANN (Input,w)£¬Input Ϊ [x1, x2, ... , xp] , Output Ϊ y. Ä¿±êº¯Êýд³É sum(Output - Predicted) + lambda* sum (w^2) £¨¶ÔÈ¨ÖØ½øÐгͷ££©. Óöµ½µÄÎÊÌâÊÇ£¬²»ÖªµÀ1stoptÔõô±íÊö Ä¿±êº¯Êý sum(Output - Predicted) + lambda* sum (w^2) . ´óÖµĴúÂëÒѾдÔÚÏÂÃæ£¬Çë¸ßÊÖÖ¸µã Variables x(1:3), y; //Define input and output, read from data Parameter w10(1:3), w11(1), w12(1), w13(1); //Weight of 1st layer of ANN Parameter w20(1:2), w21(1:2), w22(1:2), w23(1:2); //Weight of 2nd layer of ANN Parameter w30(1), w31(1), w32(1); //Weight of 3rd layer of ANN Parameter w40(1), w41(1); //Weight of last layer Constant lambda=10; //penalty rate ParameterDomain = [-10,10]; //construct a neural network //such that predicted = ANN (input, w) ConstStr s11=1*w10[1] + x[1]*w11[1]; ConstStr s12=1*w10[2] + x[2]*w12[1]; ConstStr s13=1*w10[3] + x[3]*w13[1]; ConstStr x11=(Exp(s11)-Exp(-s11))/(Exp(s11)+Exp(-s11)); ConstStr x12=(Exp(s12)-Exp(-s12))/(Exp(s12)+Exp(-s12)); ConstStr x13=(Exp(s13)-Exp(-s12))/(Exp(s13)+Exp(-s13)); ConstStr s21=1*w20[1] + x11*w21[1] + x12*w22[1] + x13*w23[1]; ConstStr s22=1*w20[2] + x11*w21[2] + x12*w22[2] + x13*w23[2]; ConstStr x21=(Exp(s21)-Exp(-s21))/(Exp(s21)+Exp(-s21)); ConstStr x22=(Exp(s22)-Exp(-s22))/(Exp(s22)+Exp(-s22)); ConstStr s31=1*w30[1] + x21*w31[1] + x22*w32[1]; ConstStr x31=(Exp(s31)-Exp(-s31))/(Exp(s31)+Exp(-s31)); ConstStr x41=1*w40[1] + x31*w41[1]; //penalize the weight ConstStr Pen=(Abs(w11[1]) + Abs(w12[1]) + Abs(w13[1])); //define the object function //my problem is HOW to define the object function!!!!!!!!!! Function y=x41 + lambda*Pen; Data; -1.681641572 1.093582247 1.100830272 -0.48571944 -0.729512802 0.650622043 0.741604004 -0.306203759 0.196395049 -1.03441539 -1.226583404 1.266410248 0.30027026 0.704671974 0.848601676 0.79683285 0.061931399 0.020863468 0.860518704 0.062366683 1.698784087 -0.61912183 0.432315092 2.082095927 1.14679752 0.427830798 -0.263937473 1.329836712 0.214324781 0.091548418 0.793921988 0.222705894 -0.830169848 -0.921937213 -0.935921936 0.019798377 0.586923897 0.59702383 0.533025371 0.943361351 1.588602336 -0.089786046 -1.069348399 1.59666387 -0.090178631 1.302791573 1.490152267 1.607087252 -1.23330565 -0.158105757 0.240996113 -1.20830822 0.614905436 -2.105734619 -0.696701884 5.049023722 -1.140435923 -1.370486438 -1.064975242 0.737797153 -0.703690339 1.41065294 -1.784497148 1.286251379 ÁíÍâ¸Ð¾õ1stoptµÄÊֲὲµÃºÃ²»È«°¡£¬Ò»Ð©»ù±¾¹Ø¼ü´ÊºÍÓï·¨¶¼Ã»Óн²Ã÷ÔõôÓã¬ÄĶù¿ÉÒÔÕÒµ½ÏêϸһµãµÄÓ÷¨£¿ лл£¡ |
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