mlpekf.m

李子滤波器和神经网络在股票系统中的态势估计与系统盈亏趋势估计中的应用

function [y,theta,thetaRecord,PRecord,OutputVariance] = mlpekf(x,d,s1,s2,Rparameter,Qparameter,KalmanP,initVar,tsteps)
% PURPOSE: To simulate a standard EKF-MLP training algorithm.
% INPUTS  : - x = The network input.
%           - d = The network target vector.
%           - s1 = Number of neurons in the hidden layer.
%           - s2 = Number of neurons in the output layer (1).
%           - Rparameter = EKF measurement noise hyperparameter.
%           - Qparameter = EKF process noise hyperparameter.
%           - KalmanP = initial EKF covariance.
%           - initVar = prior variance of the weights.
%           - tsteps = Number of time steps (input error checking).
% OUTPUTS : - y = The network output.
%           - theta = The final weights.
%           - thetaRecord = The weights at each time step.
%           - PRecord = The EKF covariance at each time step.
%           - OutputVariance = The innovations covariance.

% AUTHOR  : Nando de Freitas - Thanks for the acknowledgement :-)
% DATE    : 08-09-98


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% CHECKING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargin < 9, error('Not enough input arguments.'); end

% Check that the size of input (x) is N by L, where N is the dimension
% of the input and L is the length of the data (number of samples).
[N,L] = size(x);
[D,L] = size(d);
if (L ~= tsteps), error('d must be of size 1x(time steps).'), end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INITIALISATION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

T  = s2*(s1+1) + s1*(N+1);                % The 1 is for the bias terms.
theta = sqrt(initVar)*(randn(T,1));                   % Parameter vector.
H = zeros(T,D);                           % Jacobian Matrix.
K = zeros(T,D);                           % Kalman Gain matrix.
P = sqrt(KalmanP)*eye(T,T);                          % Weight covariance matrix.
R = Rparameter*eye(D);                    % Measurement noise covariance.
Q = Qparameter*eye(T,T);                  % Process noise covariance.
o1 = zeros(s1,1);
y = zeros(s2,L);
w2 = zeros(s2,s1+1);
w1 = zeros(s1,N+1);
thetaRecord=zeros(T,L);
PRecord=zeros(T,T,L);
OutputVariance=zeros(1,L);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%% MAIN SAMPLES LOOP %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for samples = 1:L,
  
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FEED FORWARD %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  % fill in weight matrices using the parameter vector: 

  for i = 1:s2,
    for j = 1:(s1+1),
      w2(i,j)= theta(i*(s1+1)+j-(s1+1),1);
    end;
  end;
  for i = 1:s1,
    for j = 1:(N+1),
      w1(i,j)= theta(s2*(s1+1) +i*(N+1)+j-(N+1),1);
    end;
  end;

  % Compute the network outputs for each layer:
  u1 = w1*[1 ; x(:,samples)]; 
  o1 = 1./(1+exp(-u1));
  u2 = w2*[1 ; o1];
  y(:,samples)=u2;  

  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FILL H %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % output layer:
    for i = 1:s2,
      for j = 1:(s1+1),
        if j==1
          H(i*(s1+1) + j - (s1+1) ,1)= 1;
        else
          H(i*(s1+1) + j - (s1+1) ,1)= o1(j-1,1);
        end;
      end;
    end;
    
    % Second layer:
    for i = 1:s1,
      for j = 1:(N+1),
        rhs = w2(1,i+1)*o1(i,1)*(1-o1(i,1));
        if j==1
          H(s2*(s1+1) + i*(N+1) + j - (N+1) ,1) = rhs;
        else
          H(s2*(s1+1) + i*(N+1) + j - (N+1) ,1)= rhs * x(j-1,samples);
        end;
      end;
    end;

  %%%%%%%%%%%%%%%%%%%%%%%%%%%%% KALMAN EQUATIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

  
  K = (P+Q) *H * ((R + H'*(P+Q)*H)^(-1));
  theta = theta + K * (d(:,samples) - y(:,samples));
  P = P -  K*H'*(P+Q) + Q;

  OutputVariance(1,samples) = R + H'*(P)*H;
  thetaRecord(:,samples)=theta;
  PRecord(:,:,samples)=P;
end;