A Quasi-ARMAX approach to modelling of non-linear systems

DOI10.1080/00207170110089770zbMath1023.93007OpenAlexW2154188986MaRDI QIDQ3151504

Kotaro Hirasawa, Kousuke Kumamaru, Jinglu Hu

Publication date: 16 October 2002

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207170110089770

zbMATH Keywords

predictionfault detectionadaptive controlnonlinear systemsTaylor expansionneurofuzzy modelsblackbox systemsquasi-ARMAX models

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Fuzzy control/observation systems (93C42) Neural networks for/in biological studies, artificial life and related topics (92B20) Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10)

Related Items (6)

Nonlinear system identification using quasi-ARX RBFN models with a parameter-classified scheme ⋮ Identification of state-dependent parameter models with support vector regression ⋮ State-dependent parameter models of non-linear sampled-data systems: a velocity-based linearization approach ⋮ Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series ⋮ A Quasi-ARMAX approach to modelling of non-linear systems ⋮ Comment on 'A quasi-ARMAX approach to the modelling of non-linear systems' by J. Hu et al.

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Analysis and design for a class of complex control systems. I: Fuzzy modelling and identification
• Multilayer feedforward networks are universal approximators
• Nonlinear black-box modeling in system identification: A unified overview
• Nonlinear black-box models in system identification: Mathematical foundations
• A Quasi-ARMAX approach to modelling of non-linear systems
• Overtraining, regularization and searching for a minimum, with application to neural networks

This page was built for publication: A Quasi-ARMAX approach to modelling of non-linear systems