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8th January 2015, 03:00 #1
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signature recogniation rbf(radial basis function) neural network at matlab
I have got two questions
hi i have been working about signature recocniation system in matlab.I decide to classify with rbfnn(radial basis function neural network) .I can do %60 in accuracy.The system must to work higher accuracy.Number of educational data is 728.(28*26).There are 28 persons signature.Each person has got 26 signatures.Number of test signature is 280.(28*10).There are 28 persons in signature recogniation system.Each person has got 10 signatures.
1-Which spread value i can chose?
eg = 0.001; % sum-squared error goal sc = 11; % spread constantnewrb(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.
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);
How can i set number of epochs?
Please help me...
Last edited by bigdogguru; 8th January 2015 at 03:10. Reason: Added CODE or SYNTAX Tags
8th January 2015, 03:00