# Can we optimize heterogeneous parameters of RBF Network using Gradient Descent?

There're three parameters in the Radial Basis Function Networks (RBFN).

1. Centers of RBFs
2. Width of RBFs
3. Weights of RBFs

It's a fact that Weights can be easily updated using a simple Gradient Descent. My question is: Can we optimize Centers and Widths of RBFs using Gradient Descent such that approximation will tend to be better.

Any suggestion is welcome.

• Cannot say for sure but I believe this might lead to rapid overfit since setting widhts and centers of gaussians is usually seen as constraining your model to fully capture variance of your data. – Adam Oudad Feb 5 '20 at 16:50