MEAN LEVELS IN NONLINEAR-ANALYSIS AND IDENTIFICATION

The effects of data pretreatment and mean levels upon nonlinear model structures is investigated. Techniques commonly used to prefilter data in linear system identification are shown to alter the model structur e in the nonlinear case. The effects of mean levels are considered in detail and a new unravelling algorithm is derived to recover the under lying system model when the offsets are external to the system. A new mapping from the time domain to the frequency domain is also introduce d for the case where offsets can be considered as an implicit part of the system.