I am struggling to understand how RBF (radial basis functions) work. My first question concerns the weights: are the learnable weights the same as the centres? So, is the algorithm essentially learning the centres? Furthermore, the slides of my deep learning class say that also the SD of the activation function is learned. Does this mean that the SD of each centre is learned or the overall SD for all centres? And as a follow up question, does this mean there are two different weights, one for the position of the centres and one for the SD of the activation functions?
And secondly, how would you initialise good weights? Should you look at the data and guess the centres for a starting point?
Thanks for your help.