www.gusucode.com > Regression with Gradient Descent > Regression with Gradient Descent/Poly_Pred.m
clc close all clear all %% Dummy System representation by Polynomial Equation Y=ax^2+bx+c; a=2,b=7,c=-4; % Training Data Creation x=-10:0.1:10; yt=2*x.^2 + 7*x -4; % yt=randn(1,length(x)); %% NN Parameters Declaration alpha=0.0002; a=randn(); b=randn(); c=randn(); epochs=1000; ind=randperm(length(x)); y=0*yt; %% NN Implementation for i=1:epochs for n = 1: length(x) y(ind(n))=a*x(ind(n)).^2 + b*x(ind(n)) + c; e(n)=(yt(ind(n))-y(ind(n))); a=a+alpha*e(n)*x(ind(n)).^2; b=b+alpha*e(n)*x(ind(n)); c=c+alpha*e(n); end I(i)=sum(e.^2); end subplot(2,1,1) plot(x,yt); hold on plot(x,(a*x.^2 + b*x +c),'r'); legend('Desired','Output','Location','Best'); xlabel('Input : Value of X'); ylabel('Output : Value of Y'); title('Input/Output Graph'); hold off subplot(2,1,2) plot(I); xlabel('Number of Epochs'); ylabel('Mean Squared Error (MSE)'); title('Cost Function'); pause(0.01) % end I(end) [a b c]