Details

Autor(en) / Beteiligte

• Li, Yang
• Wei, Hua-Liang
• Billings, Stephen. A.
• Sarrigiannis, P.G.

Titel

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

Ist Teil von

• International journal of systems science, 2016-08, Vol.47 (11), p.2671-2681

Ort / Verlag

Taylor & Francis

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Taylor & Francis Journals Auto-Holdings Collection

Beschreibungen/Notizen

• The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.