Signature recogniation rbf(radial basis function) neural network at matlab

eg = 0.001; % sum-squared error goal
sc = 11;    % spread constant

newrb(X,T,GOAL,SPREAD,MN,DF) takes these arguments,
X - RxQ matrix of Q input vectors.
T - SxQ matrix of Q target class vectors.
GOAL - Mean squared error goal, default = 0.0.
SPREAD - Spread of radial basis functions, default = 1.0.
MN - Maximum number of neurons, default is Q.
DF - Number of neurons to add between displays, default = 25.
and returns a new radial basis network.

The larger that SPREAD is the smoother the function approximation
will be. Too large a spread means a lot of neurons will be
required to fit a fast changing function. Too small a spread
means many neurons will be required to fit a smooth function,
and the network may not generalize well. Call newrb with
different spreads to find the best value for a given problem.

Click to expand...

     X = [1 2 3];
     T = [2.0 4.1 5.9];
     net = newrb(X,T);
     Y = net(X)

eg = 0.001; % sum-squared error goal
sc = 11;    % spread constant
net = newrb(input,target,eg,sc);