Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model
2019 (English)In: 2019 Signal Processing Symposium, SPSympo 2019, Institute of Electrical and Electronics Engineers (IEEE) , 2019, p. 227-231Conference paper, Published paper (Refereed)
Abstract [en]
The paper presents the derivation of Yule-Walker equations for the nonlinear autoregressive model NAR(p) of a time-series. The proposed method allows for easy calculation of parameters of the model. In the determined equations, higher order statistics are used, instead of autocovariances. For the linear autoregressive model AR(p), the standard Yule-Walker equations are directly based on autocovariances of time-series, or equivalently autocorrelations if the equations are rescaled. Unfortunately, it does not apply for nonlinear model. The authors show a compact matrix notation of Yule-Walker equations for the nonlinear autoregressive model with the nonlinearity of polynomial type of degree 2, with the use of higher order statistics up to 4th order, and numerical examples for electromyography signals for different hand movements.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2019. p. 227-231
Keywords [en]
autoregressive model, higher order statistics, nonlinear model, time-series, Yule-Walker equations, Control nonlinearities, Nonlinear systems, Signal processing, Time series, Auto regressive models, Autocovariances, Electromyography signals, Linear autoregressive model, Matrix notation, Non-linear model, Nonlinear autoregressive model, Nonlinear equations
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-268395DOI: 10.1109/SPS.2019.8882057Scopus ID: 2-s2.0-85074919244OAI: oai:DiVA.org:kth-268395DiVA, id: diva2:1426349
Conference
2019 Signal Processing Symposium, SPSympo 2019, 17 September 2019 through 19 September 2019
Note
Part of ISBN 9781728117157
QC 20200424
2020-04-242020-04-242024-03-15Bibliographically approved