Radial Basis Function

Where, $i$ is the number of outputs ($1$) and $j$ is the number of inputs $D$

Key Model Selection Decisions:

1. Number of RBF units (hidden neurons) - How many radial basis functions to use in the hidden layer
2. RBF centers - Where to position each RBF unit in the input space (the centers of the Gaussian functions)
3. RBF widths (σ or spread parameters) - The width/spread of each radial basis function, which controls how much of the input space each unit responds to
4. Output weights ($w_{i,j}^{\text{out}}$) - The weights connecting the RBF units to the output layer