the ouput nodes are a threshold function of a linear combination of inputs from the previous layer; in a single layered artificial neural network, the output is a linear combination of the input and if the ann has a n_1-n_2-...-n_j-n_(j+1) structure then the n_(j+1) output layers are all a threshold function of a linear combination of the outputs geerated from layer j which has n_j nodes. what is done with the outputs to obtain an approximation to a function?(adsbygoogle = window.adsbygoogle || []).push({});

say you have three output nodes and after you apply the threshold function, let's say it's sigmoid in this case, you get three outputs 0.2, 0.3, and 0.9. what do you do with those numbers if you're trying to approximate a function g?

or let's say you have one ouput node and after you apply the threshold function f to the dot product of the current weight vector and the reults of the previous layer, and you get 0.2. what do you do with that output to approximate a function?

or in general, how would you approximate the function x^2 using an ann? say on the interval [0,1] or [-1,1]...

in all the references i've seen, they go into depth about the error functions, back propogation and how to update the weights, blah blah blah but i'm failing to see where they explain how exactly one uses an ann to fit data.

my semi-ultimate goal would be to use an ann to approximate the fractional iterates of a function...

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# Basic question on neural networks

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