Simple recursive algorithm for linear-in-the-parameters nonlinear model identification

DOI10.1007/S11432-009-0172-ZzbMath1182.93117OpenAlexW2108426138MaRDI QIDQ848360

Xiuxia Du, Pingkang Li, Taotao Jin

Publication date: 3 March 2010

Published in: Science in China. Series F (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11432-009-0172-z

zbMATH Keywords

RBFMoore-Penrose inverseNARXnonlinear dynamic systemlinear-in-the-parameters models

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Cites Work

• Unnamed Item
• A two-stage algorithm for identification of nonlinear dynamic systems
• Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints
• Using a fast RLS adaptive algorithm for efficient speech processing
• An alternative proof for the recursive formulae for computing the Moore--Penrose \(M\)-inverse of a matrix
• Structure identification of nonlinear dynamic systems - A survey on input/output approaches
• Neural networks for nonlinear dynamic system modelling and identification
• Orthogonal least squares methods and their application to non-linear system identification
• Givens rotation based fast backward elimination algorithm for RBF neural network pruning
• Oscillation and Chaos in Physiological Control Systems
• Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks
• General model-set design methods for multiple-model approach

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