wnetarx.m

新的神经网络算法源程序

function [th, mseif, flopif, timeif] = ...
         wnetarx(yu, nn, nbwavelon, max_epoch, initmode, min_nbw, levels)
%Wnetarx: Compute nonlinear ARX models using WaveNet
%
% THETA = wnetarx(Z, nn, nbwavelon, max_epoch, initmode, min_nbw, levels)
%
% THETA: returned as the estimated parameters of the WaveNet-ARX model
% Z : the output-input data Z=[y u], with y and u as column vectors.
%     For multi-input systems Z=[y u1 u2 .. um]. For time series Z=y only.
% nn: nn = [na nb nk], the orders and delays. For a time series, nn=na only.
%
% nbwavelon: number of wavelets used to construct the WaveNet
% max_epoch: maximum number of epochs of training
% initmode: Initialization mode, default is 2
%             initmode=1:  residual based selection
%             initmode=2:  stepwise selection by orthogonalization
%             initmode=3:  backward elimination
%           If initmode=THETA, a matrix containing WaveNet parameters,
%           then these parameters are used to initialize the WaveNet.
%           This mode is useful for retrain WaveNets.
%
% min_nbw: minimum number of input patterns each wavelon should "cover"
% levels: number of scale levels scanned during initialization
%
%The arguments min_nbw and levels are optional.

%By Qinghua Zhang. March, 1994.

if nargin < 3, nbwavelon = []; end;
if nargin < 4, max_epoch = []; end;
if nargin < 5, initmode = []; end;
if nargin < 6, min_nbw = []; end;
if nargin < 7, levels = []; end;

if length(initmode) > 1
  [t0,d0,w0,c0,b0, nn]=decompth(initmode); nbwavelon=length(w0);
  initmode = 0;
else
  t0=[]; d0=[]; w0=[]; c0=[]; b0=[];
end

% Generate the regression matrix 
[y, x] = io2reg(yu, nn);

[t,d,w,c,b, mseif, flopif, timeif] = trainwn(x', y', nbwavelon, max_epoch, ...
              initmode, min_nbw, levels, t0, d0, w0, c0, b0);

nbwavelon = length(w);

nbvar = length(c);
thr = 2*nbvar + 5;
nnlen = size(nn,2);
thc = max([3+nnlen nbwavelon  nbvar+1 7]);

th = zeros(thr, thc);

th(1,1:(3+nnlen)) = [nbwavelon nbvar nnlen nn];
if nbwavelon>0
  th(2:nbvar+1,1:nbwavelon) = t;
  th(nbvar+2:2*nbvar+1,1:nbwavelon) = d;
  th(2*nbvar+2,1:nbwavelon) = w;
end
th(2*nbvar+3, 1:nbvar) = c;
th(2*nbvar+3, nbvar+1) = b;
th(2*nbvar+4, 1:6) = clock;
th(2*nbvar+5,1:7) = abs('WNETARX');