% Here input x and targets t define a simple function that you can plot:
x = [0 1 2 3 4 5 6 7 8];
t = [0 0.84 0.91 0.14 -0.77 -0.96 -0.28 0.66 0.99];
plot(x,t,'o')
% Here feedforwardnet creates a two-layer feed-forward network. The network has one hidden layer with ten neurons.
net = feedforwardnet(10);
net = configure(net,x,t); % Selection of the optimal weights
% The network is trained and then resimulated.
net = train(net,x,t);
y2 = net(x);
plot(x,t,'o',x,y2,'*')
perf = perform(net,t,y2)
squar_err = (t - y2)*(t - y2)'/length(t) % squared error