steepestdescent.m

內涵模糊理論與類神經網路的程式碼...提供初學者做研究參考

% ==========================================================
% 
%           Neural Networks A Classroom Approach
%                     Satish Kumar
%             Copyright Tata McGraw Hill, 2004
%
%     MATLAB code that implements the steepest descent 
%     learning law
%               Reference: Table 5.9;Page 146
%
% ==========================================================

% PROGRAM FOR STEEPEST DESCENT LEARNING

load data.txt
max_points = size(data',2);
x=data(:,1)' + 0.5;
d=data(:,2)' + 0.5;

eta = .01;							% Set learning rate
R=zeros(2,2);						% Initialize correlation matrix
X = [ones(1,max_points);x];	% Augment input vectors
% Calculate cross-correlations and target expectations
P = (sum([d.*X(1,:); d.*X(2,:)],2))/max_points;
D = (sum(d.^2))/max_points;

for k =1:max_points
  R = R+X(:,k)*X(:,k)';			% Compute R
end

R = R/max_points;
weiner=inv(R)*P;					% Compute the Weiner solution
errormin = D - P'*inv(R)*P;	% Find the minimum error

shift1 = linspace(-12,12, 21);% Generate a weight space matrix
shift2 = linspace(-9,9, 21);
for i = 1:21										% Compute a weight matrix about
  shiftwts(1,i) =  weiner(1)+shift1(i);	% the Weiner solution
  shiftwts(2,i) = weiner(2)+shift2(i);
end

for i=1:21							% Compute the error matrix 
  for j = 1:21						% to plot the error contours
      error(i,j) = sum((d - (shiftwts(1,i) + x.*shiftwts(2,j))).^2);
  end
end
error = error/max_points;

figure								
plot(weiner(1),weiner(2),'*k')% Plot the error contours
hold on
[lab,lab1]=contour(shiftwts(1,:), shiftwts(2,:),error,10);
clabel(lab);
hold on

w=[-3.9 6.27]';
w0=w;

for loop = 1:500		% Perform descent for 500 iterations
  w = w + eta*(-2*(R*w-P));
  wts1(loop)=w(1);
  wts2(loop)=w(2);
end

wts1=[w0(1) wts1];
wts2=[w0(2) wts2];

plot(wts1,wts2,'r')   
axis([-12 12 -10 8]);
grid on
xlabel('\itw_0');
ylabel('\itw_1');