Neural-like networks for replication of periodic signals

The paper describes the concepts and background theory for the analysis of a neural-like network for the replication of periodic signals containing a finite number of distinct frequency components. The approach is based on a two stage process consisting of a learning phase when the network is driven by the required signal followed by a replication phase where the network operates in an autonomous feedback mode whilst continuing to generate the required signal to a desired accuracy for a specified time. The analysis draws on available control theory and, in particular, on concepts from model reference adaptive control.