% Example Est.8.3 for Illustrating use of ANN for Nonlinear Data driven
% Estimation
% Programmed by B Joseph Washington University April 1993
% This requires use Neural Network Toolbox from Matlab
%
clc
echo on
clg
% read data
load ch19ex6_data
est81=ch19ex6_data
% split array into secondary and primary measurements
%
P=est81(:,1:4);P=P';  % This is the array of secondary measurements
                      % each column of P represents one set of measurements
T=est81(:,5);T=T';     % This is the row of compositions corresponding to each
                      % set of secondary measurements
%
[R,Q]=size(P);
S1=6;
[S2,Q]=size(T);
[W1,B1]=nwlog(S1,R); % random initialization of weights
[W2,B2]=rands(S2,S1); % random initilizaiton
disp_freq=500; % how often result is displayed
max_epoch=5000;
err_goal=.01;
lr=0.01;
momentum=.95;
err_ratio=1.04;
TP=[disp_freq max_epoch err_goal lr momentum err_ratio];
[W1,B1,W2,B2, epochs, TR]=trainbpm(W1,B1,'logsig',W2,B2,'purelin',P,T,TP);
A1=purelin(W2*logsig(W1*P,B1),B2);
plot(A1,T,'+',T,T);
sse=norm(T-A1)^2;ssy=norm(T)^2;Rsquared=1-sse/ssy;
sse,Rsquared
load ch19ex6_test_data;
est81=ch19ex6_test_data;
% split array into secondary and primary measurements
%
P=est81(:,1:4);P=P';  % This is the array of secondary measurements
                      % each column of P represents one set of measurements
T=est81(:,5);T=T';     % This is the row of compositions corresponding to each
A1=purelin(W2*logsig(W1*P,B1),B2);
plot(A1,T,'+',T,T);
sse=norm(T-A1)^2;ssy=norm(T)^2;Rsquared=1-sse/ssy;
sse,Rsquared