optrinit.m

类神经网路─MATLAB的应用(范例程式)

% ------------------------------>  OPTRINIT.M  <------------------------------
% Initialization file for "opttrain"


% ----------      Switches       -----------
simul      = 'simulink';     % System specification (simulink/matlab/nnet)
method     = 'ff';           % Training algorithm (ff/ct/efra)
refty      = 'siggener';     % Reference signal (siggener/<var. name>)


% ------    General Initializations  -------
Ts = 0.20;                   % Sampling period (in seconds)
samples = 200 ;              % Number of samples in each epoch


% --  System to be Controlled (SIMULINK) --
integrator= 'ode45';         % Name of dif. eq. solver (f. ex. ode45 or ode15s)
sim_model = 'spm1';          % Name of SIMULINK model


% ---  System to be Controlled (MATLAB)  --
mat_model = 'springm';       % Name of MATLAB model
model_out = 'smout';         % Output equation (function of the states)
x0        = [0;0];           % Initial states
 

% ----- Neural Network Specification ------
% The "forward model file" must contain the following variables which together
% define a NNARX-model:
% NN, NetDeff, W1f, W2f
% and the "controller network file" must contain
% NetDefc, W1c, W2c
% (i.e. regressor structure, architecture definition, and weight matrices)
nnforw = 'forward';          % Name of file containing forward model
nnctrl = 'initopt';          % Name of file containing initial controller net


% ------------ Reference filter ---------------
Am = [1];                    % Filter denominator
Bm = [1];                    % Filter numerator (begins in z^0)


% ------------ Training parameters -----------
maxiter = 7;                 % Maximum number of epochs
rho     = 1e-3;              % Penalty on squared differenced controls


% --- Forgetting factor algorithm (ff) ---
% trparms = [lambda p0]
%    lambda = forgetting factor (suggested value: 0.995)
%    p0     = Covariance matrix diagonal (1-10)
%    
% --- Constant trace algorithm (ct) ---
% trparms = [lambda alpha_max alpha_min]
%    lambda = forgetting factor (suggested value: 0.995)
%    alpha_max = Max. eigenvalue of covariance matrix (100)
%    alpha_min = Min. eigenvaule of covariance matrix (0.001)
%    
% --- Exponential Forgetting and Restting Algorithm (efra) ---
% trparms = [alpha beta delta lambda]
%    Suggested values:
%    alpha = 0.5-1
%    beta = 0.001
%    delta = 0.001
%    lambda = 0.98
trparms = [0.995 10];
%trparms = [0.995 100 0.001];
%trparms = [1 0.001 0.001 0.98];


% ------------ Reference signal ------------
% Reference generated by the signal generator
dc      = 0;                 % DC-level
sq_amp  = 1;                 % Amplitude of square signals (row vector)
sq_freq = 0.1;               % Frequency of square signals (column vector)
sin_amp = [0];               % Amplitude of sine signals  (row vector)
sin_freq= [0]';              % Frequency of sine signals   (column vector)
Nvar  = 0';                  % Variance of white noise signal


% ------- Specify data vectors to plot --------
% Notice that all strings in plot_a and plot_b resp. MUST have the same length
plot_a(1,:) = 'ref_data    ';
plot_a(2,:) = 'y_data      ';
plot_b(1,:) = 'u_data';