incbp.m

来自「基于MATLAB的神经网络非线性系统辨识软件包.」· M 代码 · 共 160 行

function [W1,W2,PI_vector,iter]=incbp(NetDef,W1,W2,PHI,Y,trparms)
% 
%  INCBP
%  -----
%        Recursive (=incremental) version of the backpropagation algorithm.
%
%  Given a set of corresponding input-output pairs and an initial network
%  [W1,W2,critvec,iter]=INCBP(NetDef,W1,W2,PHI,Y,trparms) trains a
%  network with recursive backpropagation.
%
%  The activation functions must be either linear or tanh. The network
%  architecture is determined by the matrix 'NetDef' consisting of two 
%  rows. The first row specifies the hidden layer while the second
%  specifies the output layer.
%
%  E.g.,    NetDef = ['LHHHH'
%                     'LL---']
%
%  (L = Linear, H = tanh)
% 
%  Notice that the bias is included as the last column in the weight
%  matrices.
%
%  See alternatively BATBP for the batch version of back-propagation.
% 
%  INPUT:
%  NetDef: Network definition 
%  W1    : Input-to-hidden layer weights. The matrix dimension is
%          dim(W1) = [(# of hidden units) * (inputs + 1)] (the 1 is due to the bias)
%          Use [] for a random initialization.
%  W2    : hidden-to-output layer weights.
%          dim(W2) = [(outputs)  *  (# of hidden units + 1)]
%          Use [] for a random initialization.
%  PHI   : Input vector. dim(PHI) = [(inputs)  *  (# of data)]
%  Y     : Output data. dim(Y) = [(outputs)  * (# of data)]
%  trparms: Data structure containing parameters associated with the
%           training algorithm (optional). Use the function SETTRAIN if
%           you do not want to use the default values.
%
%  OUTPUT:
%  W1, W2   : Weight matrices after training.
%  critvec  : Vector containing the criterion evaluated after each iteration.
%  iter     : # of iterations.
% 
%  Programmed by : Magnus Norgaard, IAU/IMM, Technical University of Denmark
%  LastEditDate  : Jan. 15, 2000
 
%----------------------------------------------------------------------------------
%--------------             NETWORK INITIALIZATIONS                   -------------
%----------------------------------------------------------------------------------
if nargin<6 | isempty(trparms) % Default training parameters
   trparms = settrain;   
   eta = trparms.eta;
   alpha = trparms.alph;
else                                    % User specified values
  if ~isstruct(trparms),
     error('''trparms'' must be a structure variable.');
  end
    if ~isfield(trparms,'infolevel')
     trparms = settrain(trparms,'infolevel','default');
  end
  if ~isfield(trparms,'maxiter')
     trparms = settrain(trparms,'maxiter','default');
  end
  if ~isfield(trparms,'critmin')
     trparms = settrain(trparms,'critmin','default');
  end
  if ~isfield(trparms,'critterm')
     trparms = settrain(trparms,'critterm','default');
  end
  if ~isfield(trparms,'eta')
     trparms = settrain(trparms,'eta','default');
  end
  eta    = trparms.eta;
end
[outputs,N] = size(Y);
[inputs,N] = size(PHI);               % # of hidden units
L_hidden = find(NetDef(1,:)=='L')';   % Location of linear hidden neurons
H_hidden = find(NetDef(1,:)=='H')';   % Location of tanh hidden neurons
L_output = find(NetDef(2,:)=='L')';   % Location of linear output neurons
H_output = find(NetDef(2,:)=='H')';   % Location of tanh output neurons
hidden = length(L_hidden)+length(H_hidden);
if isempty(W1) | isempty(W2),           % Initialize weights if nescessary
  W1 = rand(hidden,inputs+1)-0.5;
  W2 = rand(outputs,hidden+1)-0.5;
end
if (size(W1,2)~=inputs+1 | size(W1,1)~=hidden |... % Check dimensions
      size(W2,2)~=hidden+1 | size(W2,1)~=outputs)
   error('Dimension mismatch in weights, data, or NetDef.');
end
PI_vector  = zeros(trparms.maxiter,1);% Vector containing the SSE for each iteration
y1 = zeros(hidden,1);
delta1 = y1;
y2 = zeros(outputs,1);
delta2 = y2;
Y_train = zeros(size(Y));
critdif = trparms.critterm+1;         % Initialize stopping variables


%---------------------------------------------------------------------------------
%-------------                   TRAIN NETWORK                       -------------
%---------------------------------------------------------------------------------
clc;
c=fix(clock);
fprintf('Network training started at %2i.%2i.%2i\n\n',c(4),c(5),c(6));

for j=1:trparms.maxiter,
  PI=0;
  for jj=1:N,
   
% ---  Compute network output (Presentation phase)  ---
    h1 = W1(:,1:inputs)*PHI(:,jj) + W1(:,inputs+1);  
    y1(H_hidden) = pmntanh(h1(H_hidden));   % 1 ./ (1 + exp(-x1));
    y1(L_hidden) = h1(L_hidden);
    
    h2 = W2(:,1:hidden)*y1 + W2(:,hidden+1);
    y2(H_output) = pmntanh(h2(H_output));
    y2(L_output) = h2(L_output);

% ---  Train network  ---
    E = Y(:,jj) - y2;                      % Training error
                                           % Delta for output layer
    delta2(H_output) = (1-y2(H_output).*y2(H_output)).*E(H_output);
    delta2(L_output) = E(L_output);
                                           % delta for hidden layer
    E1 = W2(:,1:hidden)'*delta2; 
    delta1(H_hidden) = (1-y1(H_hidden).*y1(H_hidden)).*E1(H_hidden);
    delta1(L_hidden) = E1(L_hidden);
   
    W2 = W2 + eta*delta2*[y1;1]';          % Update weights between hidden and ouput
    W1 = W1 + eta*delta1*[PHI(:,jj);1]';   % Update weights between input and hidden
   
    Y_train(:,jj)=y2;
    PI = PI + E'*E;                        % Update performance index (SSE)
  end
  
%>>>>>>>>>>>>>>>>>>>>>>       UPDATES FOR NEXT ITERATION       <<<<<<<<<<<<<<<<<<<
  PI = PI/(2*N);
  PI_vector(j)=PI;
  if j>1, 
     critdif  = abs(PI_vector(j-1)-PI);    % Criterion difference
  end   
  switch(trparms.infolevel)                                % Print on-line inform
       case 1
          fprintf('iteration # %i   W=%4.3e  critdif=%3.2e\n',j,PI,critdif);
       otherwise
          fprintf('iteration # %i   W = %4.3e\r',j,PI);
  end
  if (PI < trparms.critmin | critdif<trparms.critterm) % Check if stop condition is satisfied
     break
  end
end
%---------------------------------------------------------------------------------
%-------------              END OF NETWORK TRAINING                  -------------
%---------------------------------------------------------------------------------
iter=j;
PI_vector = PI_vector(1:iter);
c=fix(clock);
fprintf('\n\nNetwork training ended at %2i.%2i.%2i\n',c(4),c(5),c(6));