小波神经网络进行短期电力负荷预测的matlab代码.doc

小波神经网络进行短期电力负荷预测的matlab代码专业技术 2008-09-21 15:48:33 阅读399 评论0 字号：大中小订阅 作者：佚名来源：转载/blog/static/32176390200841092451795/发布时间：2008-5-31 22:46:51clear;P= 0.2286 0.2090 0.0442 0.3690 0.2603 0.0359 0.0724 0.1759 0.2634;0.1292 0.0947 0.0880 0.2222 0.1715 0.1149 0.1909 0.2347 0.2258;0.0720 0.1393 0.1147 0.0562 0.0702 0.1230 0.1340 0.1829 0.1165;0.1529 0.8700 0.0563 0.5157 0.2711 0.5460 0.2409 0.1811 0.1124;0.1335 0.2558 0.3347 0.1872 0.1491 0.1977 0.2842 0.2922 0.1074;0.0733 0.0900 0.1100 0.1614 0.1330 0.1248 0.0450 0.0655 0.0657;0.1159 0.0771 0.1453 0.1425 0.0968 0.0624 0.0824 0.0774 0.0610;0.0940 0.0882 0.0429 0.1506 0.1911 0.0832 0.1064 0.2237 0.2623;0.0522 0.0393 0.1818 0.1310 0.2545 0.1640 0.1909 0.2056 0.2588;0.1345 0.1430 0.0378 0.0500 0.0871 0.1002 0.1586 0.0925 0.1155;0.0090 0.0126 0.0092 0.0078 0.0060 0.0059 0.0116 0.0078 0.0050;0.1260 0.1670 0.2251 0.0348 0.1793 0.1503 0.1698 0.1852 0.0978;0.3619 0.2450 0.1560 0.0451 0.1001 0.1837 0.3644 0.3501 0.1511;0.0690 0.0508 0.0852 0.0707 0.0789 0.1295 0.2718 0.1680 0.2273;0.1828 0.1328 0.0670 0.0880 0.0909 0.0700 0.2494 0.2668 0.3220;T=1 1 1 0 0 0 0 0 0;0 0 0 1 1 1 0 0 0;0 0 0 0 0 0 1 1 1;threshold=0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;net=newff(threshold,15,3,morlet,logsig,traingda);net.trainParam.epochs=500;net.trainParam.goal=0.001;LP.Ir=0.01;s=15;r=15;r,q = size(P);pmin = min(P);pmax = max(P)magw =0.1*s(1/r);w = magw*randnr(s,r);b = magw*rands(s,1);b1=rands(s,1);rng = pmax-pmin;mid = 0.5*(pmin+pmax);w=2*w./(ones(s,1)*rng);b = b - w*mid;a=w*rng/2.45;t=w*mid;net.IW1,1=w;net.b1,1=b;net.LW2,1=0.01*randnr(3,s);net.b2,1=0.1*randnc(3,1);net=train(net,P,T);w1=net.IW1,1;b1=net.b1,1;w2=net.LW2,1;b2=net.b2,1;%保存网络训练结果fww1=fopen(w11.dat,w);fwb1=fopen(b11.dat,w);fww2=fopen(w22.dat,w);fwb2=fopen(b22.dat,w);fprintf(fww1,%6.4f %6.4fn,w1);fprintf(fwb1,%6.4f %6.4fn,b1);fprintf(fww2,%6.4f %6.4fn,w2);fprintf(fwb2,%6.4f %6.4fn,b2);fclose(fww1);fclose(fwb1);fclose(fww2);fclose(fwb2);g= 0.2101 0.2593 0.2599;0.095 0.18 0.2235;0.1298 0.0711 0.1201;0.1359 0.2801 0.1171;0.2601 0.1501 0.1102;0.1001 0.1298 0.0683;0.0753 0.1001 0.0621;0.089 0.1891 0.2597;0.0389 0.2531 0.2602;0.1451 0.0875 0.1167;0.0128 0.0058 0.0048;0.159 0.1803 0.1002;0.2452 0.0992 0.1521;0.0512 0.0802 0.2281;0.1319 0.1002 0.3205;y=sim(net,g)=问题来源：/forum/thread-48742-1-1.html程序一：GA训练BP权值的主函数function net=GABPNET(XX,YY)%-% GABPNET.m% 使用遗传算法对BP网络权值阈值进行优化，再用BP算法训练网络%-%数据归一化预处理nntwarn offXX=premnmx(XX);YY=premnmx(YY);%创建网络net=newff(minmax(XX),19,25,1,tansig,tansig,purelin,trainlm);%下面使用遗传算法对网络进行优化P=XX;T=YY;R=size(P,1);S2=size(T,1);S1=25;%隐含层节点数S=R*S1+S1*S2+S1+S2;%遗传算法编码长度aa=ones(S,1)*-1,1;popu=50;%种群规模initPpp=initializega(popu,aa,gabpEval);%初始化种群gen=100;%遗传代数%下面调用gaot工具箱，其中目标函数定义为gabpEvalx,endPop,bPop,trace=ga(aa,gabpEval,initPpp,1e-6 1 1,maxGenTerm,gen,. normGeomSelect,0.09,arithXover,2,nonUnifMutation,2 gen 3);%绘收敛曲线图figure(1)plot(trace(:,1),1./trace(:,3),r-);hold onplot(trace(:,1),1./trace(:,2),b-);xlabel(Generation);ylabel(Sum-Squared Error);figure(2)plot(trace(:,1),trace(:,3),r-);hold onplot(trace(:,1),trace(:,2),b-);xlabel(Generation);ylabel(Fittness);%下面将初步得到的权值矩阵赋给尚未开始训练的BP网络W1,B1,W2,B2,P,T,A1,A2,SE,val=gadecod(x);net.LW2,1=W1;net.LW3,2=W2;net.b2,1=B1;net.b3,1=B2;XX=P;YY=T;%设置训练参数net.trainParam.show=1;net.trainParam.lr=1;net.trainParam.epochs=50;net.trainParam.goal=0.001;%训练网络net=train(net,XX,YY);程序二：适应值函数function sol, val = gabpEval(sol,options)% val - the fittness of this individual% sol - the individual, returned to allow for Lamarckian evolution% options - current_generationload data2nntwarn offXX=premnmx(XX);YY=premnmx(YY);P=XX;T=YY;R=size(P,1);S2=size(T,1);S1=25;%隐含层节点数S=R*S1+S1*S2+S1+S2;%遗传算法编码长度for i=1:S, x(i)=sol(i);end;W1, B1, W2, B2, P, T, A1, A2, SE, val=gadecod(x);程序三：编解码函数function W1, B1, W2, B2, P, T, A1, A2, SE, val=gadecod(x)load data2nntwarn offXX=premnmx(XX);YY=premnmx(YY);P=XX;T=YY;R=size(P,1);S2=size(T,1);S1=25;%隐含层节点数S=R*S1+S1*S2+S1+S2;%遗传算法编码长度% 前R*S1个编码为W1for i=1:S1, for k=1:R, W1(i,k)=x(R*(i-1)+k); endend% 接着的S1*S2个编码（即第R*S1个后的编码）为W2for i=1:S2, for k=1:S1, W2(i,k)=x(S1*(i-1)+k+R*S1); endend% 接着的S1个编码（即第R*S1+S1*S2个后的编码）为B1for i=1:S1, B1(i,1)=x(R*S1+S1*S2)+i);end% 接着的S2个编码（即第R*S1+S1*S2+S1个后的编码）为B2for i=1:S2, B2(i,1)=x(R*S1+S1*S2+S1)+i);end% 计算S1与S2层的输出A1=tansig(W1*P,B1);A2=purelin(W2*A1,B2);% 计算误差平方和SE=sumsqr(T-A2);val=1/SE; % 遗传算法的适应值上述程序需要调用gaot工具箱=initializega 函数的代码:function pop = initializega(num, bounds, evalFN,evalOps,options)% function pop=initializega(populationSize, variableBounds,evalFN,% evalOps,options)% initializega creates a matrix of random numbers with% a number of rows equal to the populationSize and a number% columns equal to the number of rows in bounds plus 1 for% the f(x) value which is found by applying the evalFN.% This is used by the ga to create the population if it% is not supplied.% pop - the initial, evaluated, random population% populatoinSize - the size of the population, i.e. the number to create% variableBounds - a matrix which contains the bounds of each variable, i.e.% var1_high var1_low; var2_high var2_low; .% evalFN - the evaluation fn, usually the name of the .m file for% evaluation% evalOps - any options to be passed to the eval function defaults % options - options to the initialize function, ie.% type prec where eps is the epsilon value% and the second option is 1 for float and 0 for binary,% prec is the precision of the variables defaults 1e-6 1% Binary and Real-Valued Simulation Evolution for Matlab GAOT V2% Copyright (C) 1998 C.R. Houck, J.A. Joines, M.G. Kay% C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function% optimization: A Matlab implementation. ACM Transactions on Mathmatical% Software, Submitted 1996.% This program is free software; you can redistribute it and/or modify% it under the terms of the GNU General Public License as published by% the Free Software Foundation; either version 1, or (at your option)% any later version.% This program is distributed in the hope that it will be useful,% but WITHOUT ANY WARRANTY; without even the implied warranty of% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the% GNU General Public License for more details. A copy of the GNU% General Public License can be obtained from the% Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.if nargin5 options=1e-6 1;endif nargin4 evalOps=;endif any(evalFN48) %Not a .m file if options(2)=1 %Float GA estr=x=pop(i,1); pop(i,xZomeLength)=, evalFN ; else %Binary GA estr=x=b2f(pop(i,:),bounds,bits); pop(i,xZomeLength)=, evalFN ; endelse %A .m file if options(2)=1 %Float GA estr= pop(i,:) pop(i,xZomeLength)= evalFN (pop(i,:),0 evalOps); else %Binary GA estr=x=b2f(pop(i,:),bounds,bits);x v= evalFN . (x,0 evalOps); pop(i,:)=f2b(x,bounds,bits) v; endendnumVars = size(bounds,1); %Number of variablesrng = (bounds(:,2)-bounds(:,1); %The variable rangesif options(2)=1 %Float GA xZomeLength = numVars+1; %Length of string is numVar + fit pop = zeros(num,xZomeLength); %Allocate the new population pop(:,1:numVars)=(ones(num,1)*rng).*(rand(num,numVars)+. (ones(num,1)*bounds(:,1);else %Binary GA bits=calcbits(bounds,options(1); xZomeLength = sum(bits)+1; %Length of string is numVar + fit pop = round(rand(num,sum(bits)+1);endfor i=1:num eval(estr);end神经网络(BP)系列(初学者系列)学习 2008-09-21 15:46:43 阅读136 评论0 字号：大中小订阅 作者：佚名来源：转载/blog/static/32176390200832993744399/发布时间：2008-5-31 22:45:48这个系列主要针对使用matlab 神经网络工具箱，对一些初学者容易理解错误的地方进行解析。神经网络每次结果不同解析神经网络每次结果不同是因为初始化的权值和阈值是随机的因为每次的结果不一样，才有可能找到比较理想的结果啊找到比较好的结果后，用命令save filename net;保存网络，可使预测的结果不会变化，调用时用命令load filename net;取p_test= ;t_test= ;t=sim(net,p_test);err=t_test-t;plot(p_test,err);选择误差小的保存网络save filename net以后调用时load filename netp_test= ;t_test= ;t=sim(net,p_test);err=t_test-t;plot(p_test,err):因为每次初始化网络时都是随机的，而且训练终止时的误差也不完全相同，结果训练后的权植和阀也不完全相同（大致是一样的），所以每次训练后的结果也略有不同举个例子，这样初始化就能使网络的输出结果是一样的，另外也可以给网络特定的权值，一种方法是把预测结果后的效果比较好的权值做为初值p=0.8726 0.9441 0;0 0 0.7093;0.7378 0.7093 0.3795;0.6416