I would like to implement a Radial Basis Function (Neural) Network.

Specifically, I would like to implement the network as described in this paper: http://www.ncbi.nlm.nih.gov/pubmed/15732389. The name of the network is GGAP-RBF. A variant which is related is M-RAN. I have also studied that paper. However, I encounter the same problem. The problem is that I have difficulty with interpreting the description of the algorithms. Because in both descriptions the author states that the network starts with no hidden neurons. According to a growing criterion the neuron is added.

Page 62. describes the algorithm. First I need to start with computing the overall network output: \begin{align} f(x_{n}) = \sum_{k = 1}^{K} \alpha_{k} \exp \left(- \frac{1}{\sigma_{k}^{2}} || x_{n} - \mu_{k} ||^{2} \right) \end{align}

However, how could I do this when no neurons are available?

If we assume that was intended we skip step 2 and move to step 3. Here we need to apply the growing criterion for neurons. I will only describe the relevant part. That is: \begin{align} ||x_{n} - \mu_{nr} || > \epsilon_{n} \end{align}

If no neurons are available how could I compute this part of the growing criterion? Should it be a zero vector because no information is available?

I assume that I am missing some information. Would someone be so kind and explain that information?


1 Answer 1


A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation appears to start by comparing the output neuron with the $k=1$, i.e., first hidden neuron. That is, the radial basis function is evaluated for statistical validity even for the first neuron.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.