Function Approximation using Neural Networks

The files NN_Layer.py and Neural_Network.py contain classes to implement a generic Multi Layer Neural Network with VLBP learning scheme.

The file Radial_Basis_Network.py contains a class to implement a Neural Network with Radial Basis Functions in the first layer.

The file Functions.py define the target functions that will be approximated by the Neural Networks.

Run the scripts -

1. "Booth Function Approximation.py" to train a neural network to approximate Booth Function

2. "Styblinski Tang Function Approximation.py" to train a neural network to approximate Styblinski-Tang Function

3. "Eggholder Function Approximation.py" to train a neural network to approximate Eggholder Function

4. "Eggholder_Function_Approximation_Using_RBFs.py" to train a RBF network to approximate Eggholder Function