gusucode.com > Regression with Gradient Descent > Regression with Gradient Descent/Log_Pred.m
clc close all clear all %% Dummy System representation by Polynomial Equation Y=a*log(x); a=5; % Training Data Creation x=-100:0.3:100; yt=5*log(x); % yt=randn(1,length(x)); %% NN Parameters Declaration alpha=0.001; a=randn(); % b=randn(); % c=randn(); epochs=10; ind=randperm(length(x)); y=0*yt; %% NN Implementation for i=1:epochs for n = 1: length(x) y(ind(n))=a*log(x(ind(n))); e(n)=(yt(ind(n))-y(ind(n))); a=a+alpha*e(n)*log(x(ind(n))); % b=b+alpha*e(n)*x(ind(n)); % c=c+alpha*e(n); end I(i)=sum(abs(e).^2); end subplot(2,1,1) plot(x,yt); hold on plot(x,(a*log(x)),'r'); legend('Desired','Output','Location','Best'); xlabel('Input : Value of X'); ylabel('Output : Value of Y'); title('Input/Output Graph'); subplot(2,1,2) plot(I); xlabel('Number of Epochs'); ylabel('Mean Squared Error (MSE)'); title('Cost Function'); I(end) [a]