Neural Computation Used for Functions Approximation

Solving a problem with a neural network a primordial task is establishing the network topology. Generally neural network topology determination is a complex problem and cannot be easily solved. Learning from examples means being able to infer the functional dependence between input and output spaces X and Z, given the knowledge of the set of examples T. From this point of view learning is equivalent to a function approximation.