How to create a transfer function with variable parameter?
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Hello, I would like to create a custom neural network with custom neurons that will have transfer functions of a type: y(x)=exp(-x/l), where l is a parameter that will be different for each neuron and I have to have ability to initialize it for each neuron separately.
This is an attempt to model a particular constitutive equation. Creating a custom network structure is not a problem (thanks to Greg Heath) !
Thank you in advance!
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Greg Heath
on 28 Feb 2016
This doesn't sound right. The default FEEDFORWARDNET configuration with TANSIG, LOGSIG, or RADBAS hidden nodes is a Universal Approximator. Exponentials don't satisfy the UA criteria.
Maybe Googling "Universal Approximator" might help you understand. I've forgotten the details).
Hope this Helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
on 4 Mar 2016
By a change of variable exp(-abs(x)/a) is equivalent to a Gaussian.
However, I don't know if it helps because I don't know why you want to do this.
Greg
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