基於小波神經網絡的交通流預測

%該代碼爲基於小波神經網絡的交通流預測代碼
% 清空環境變量
clc
clear
%網絡參數配置
load traffic_flux input output input_test output_test
M=size(input,2); %輸入節點個數
N=size(output,2); %輸出節點個數
n=6; %隱形節點個數
lr1=0.01; %學習概率
lr2=0.001; %學習概率
maxgen=100; %迭代次數
%權值初始化
Wjk=randn(n,M);Wjk_1=Wjk;Wjk_2=Wjk_1;
Wij=randn(N,n);Wij_1=Wij;Wij_2=Wij_1;
a=randn(1,n);a_1=a;a_2=a_1;
b=randn(1,n);b_1=b;b_2=b_1;
%節點初始化
y=zeros(1,N);
net=zeros(1,n);
net_ab=zeros(1,n);
%權值學習增量初始化
d_Wjk=zeros(n,M);
d_Wij=zeros(N,n);
d_a=zeros(1,n);
d_b=zeros(1,n);
%輸入輸出數據歸一化
[inputn,inputps]=mapminmax(input');
[outputn,outputps]=mapminmax(output'); 
inputn=inputn';
outputn=outputn';
error=zeros(1,maxgen);
%網絡訓練
for i=1:maxgen    
    %誤差累計
    error(i)=0;
    % 循環訓練
    for kk=1:size(input,1)
        x=inputn(kk,:);
        yqw=outputn(kk,:);
        for j=1:n
            for k=1:M
                net(j)=net(j)+Wjk(j,k)*x(k);
                net_ab(j)=(net(j)-b(j))/a(j);
            end
            temp=mymorlet(net_ab(j));
            for k=1:N
                y=y+Wij(k,j)*temp;   %小波函數
            end
        end
        %計算誤差和
        error(i)=error(i)+sum(abs(yqw-y));
        %權值調整
        for j=1:n
            %計算d_Wij
            temp=mymorlet(net_ab(j));
            for k=1:N
                d_Wij(k,j)=d_Wij(k,j)-(yqw(k)-y(k))*temp;
            end
            %計算d_Wjk
            temp=d_mymorlet(net_ab(j));
            for k=1:M
                for l=1:N
                    d_Wjk(j,k)=d_Wjk(j,k)+(yqw(l)-y(l))*Wij(l,j) ;
                end
                d_Wjk(j,k)=-d_Wjk(j,k)*temp*x(k)/a(j);
            end
            %計算d_b
            for k=1:N
                d_b(j)=d_b(j)+(yqw(k)-y(k))*Wij(k,j);
            end
            d_b(j)=d_b(j)*temp/a(j);
            %計算d_a
            for k=1:N
                d_a(j)=d_a(j)+(yqw(k)-y(k))*Wij(k,j);
            end
            d_a(j)=d_a(j)*temp*((net(j)-b(j))/b(j))/a(j);
        end
        %權值參數更新      
        Wij=Wij-lr1*d_Wij;
        Wjk=Wjk-lr1*d_Wjk;
        b=b-lr2*d_b;
        a=a-lr2*d_a;
        d_Wjk=zeros(n,M);
        d_Wij=zeros(N,n);
        d_a=zeros(1,n);
        d_b=zeros(1,n);
        y=zeros(1,N);
        net=zeros(1,n);
        net_ab=zeros(1,n);
        Wjk_1=Wjk;Wjk_2=Wjk_1;
        Wij_1=Wij;Wij_2=Wij_1;
        a_1=a;a_2=a_1;
        b_1=b;b_2=b_1;
    end
end
% 網絡預測
%預測輸入歸一化
x=mapminmax('apply',input_test',inputps);
x=x';
yuce=zeros(92,1);
%網絡預測
for i=1:92
    x_test=x(i,:)
    for j=1:1:n
        for k=1:1:M
            net(j)=net(j)+Wjk(j,k)*x_test(k);
            net_ab(j)=(net(j)-b(j))/a(j);
        end
        temp=mymorlet(net_ab(j));
        for k=1:N
            y(k)=y(k)+Wij(k,j)*temp ; 
        end
    end
    yuce(i)=y(k);
    y=zeros(1,N);
    net=zeros(1,n);
    net_ab=zeros(1,n);
end
%預測輸出反歸一化
ynn=mapminmax('reverse',yuce,outputps);
%結果分析
figure(1)
plot(ynn,'r*:')
hold on
plot(output_test,'bo--')
title('預測交通流量','fontsize',12)
legend('預測交通流量','實際交通流量','fontsize',12)
xlabel('時間點')
ylabel('交通流量')
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