bsb.m

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% ==========================================================
% 
%           Neural Networks A Classroom Approach
%                     Satish Kumar
%             Copyright Tata McGraw Hill, 2004
%
%         MATLAB code that simulates a BSB network
%             Reference: Table 10.6;Page 424
%
% ==========================================================

% Initialize variables
delta = 0;
gamma = 1;
alpha = 1;
lim_u = 1;
lim_l = -1;
lambda1 = 0.04;
lambda2 = 0.03;

% Specify vectors to encode
x1 = [1  -1]';
x2 = [1  1]';

% Normalize them
x1_n = x1/sqrt(2);
x2_n = x2/sqrt(2);

% Encode them
W = lambda1*x1_n * x1_n' + lambda2*x2_n * x2_n';

figure(1);
hold on;
grid on;

% Start at an initial point of (-1,-1) and go to (1,1)
% in steps of 0.1
x = -1;
y = -1;
inc = 0.1;

while(x <=1)
    while(y <= 1);
        s(:,1) = [x y]';
        for i = 2:150
          act = gamma * s(:,i-1)' + alpha * s(:,i-1)' * W + delta * s(:,1)';
          for j=1:2            
            if (act(j) <= lim_l) 
              s(j,i) = lim_l;
            elseif (act(j) >= lim_u) 
              s(j,i) = lim_u;
            else s(j,i) = act(j);
            end
          end
        end
        
        if ((s(1,i) == 1) & (s(2,i) == 1)) plot(s(1,:), s(2,:), 'k-.');
        elseif ((s(1,i) == 1) & (s(2,i) == -1)) plot(s(1,:), s(2,:), 'k');
        elseif ((s(1,i) == -1) & (s(2,i) == 1)) plot(s(1,:), s(2,:), 'k');
        elseif ((s(1,i) == -1) & (s(2,i) == -1)) plot(s(1,:), s(2,:), 'k-.'); 
        end
        
        y = y + inc;
    end
    y = -1;
    x = x + inc;
end