小波神经网络进行短期电力负荷预测的matlab代码

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 off

XX=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gabpEval

[x,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 on

plot(trace(:,1),1./trace(:,2),'b-');

xlabel('Generation');

ylabel('Sum-Squared Error');

figure(2)

plot(trace(:,1),trace(:,3),'r-');

hold on

plot(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.LW{2,1}=W1;

net.LW{3,2}=W2;

net.b{2,1}=B1;

net.b{3,1}=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_generation]

load data2

nntwarn off

XX=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 data2

nntwarn off

XX=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W1

for i=1:S1,

    for k=1:R,

      W1(i,k)=x(R*(i-1)+k);

    end

end

% 接着的S1*S2个编码（即第R*S1个后的编码）为W2

for i=1:S2,

   for k=1:S1,

      W2(i,k)=x(S1*(i-1)+k+R*S1);

   end

end

% 接着的S1个编码（即第R*S1+S1*S2个后的编码）为B1

for 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B2

for 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 nargin<5

  options=[1e-6 1];

end

if nargin<4

  evalOps=[];

end

if any(evalFN<48) %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 ';'];

  end

else %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];'];  

    end

end

numVars     = size(bounds,1);                 %Number of variables

rng         = (bounds(:,2)-bounds(:,1))'; %The variable ranges'

if 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));

end

for i=1:num

  eval(estr);

end