Neural spectral composition for function approximation

An innovative neural-based approach for function approximation is proposed by means of the spectral analysis of the function y(x) to be approximated. Approximation is obtained by the spectral composition of the approximating function yˆ(x) performed by a neural network. The synthesis procedure for the neural network ensures the minimal dimension of the network itself, according to the chosen approximation error. Parameters adaptation is very fast. Since most of the structure is independent from the particular approximated function, the circuit architecture implementing the network can be easily modularized for architecture adaptation