Extended model set, global data and threshold model identification of severely non-linear systems

DOI10.1080/00207178908953473zbMath0686.93092OpenAlexW2132856552MaRDI QIDQ4205380

Sheng Chen, Stephen A. Billings

Publication date: 1989

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

Full work available at URL: https://eprints.soton.ac.uk/251148/1/778742008_content.pdf

zbMATH Keywords

orthogonal decompositionparsimonious modelsparameter estimation algorithmstructure determinationseverely nonlinear systems

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (21)

DISCRETE WAVELET MODELS FOR IDENTIFICATION AND QUALITATIVE ANALYSIS OF CHAOTIC SYSTEMS ⋮ Integral minimum variance-like control for pooled non-linear representations with application to an aircraft system ⋮ Non-linear systems in the frequency domain: Energy transfer filters ⋮ Comparison of two construction algorithms for local model networks ⋮ On-line structure detection and parameter estimation with exponential windowing for nonlinear systems ⋮ Non-linear systems identification using radial basis functions ⋮ Output frequency response function of nonlinear Volterra systems ⋮ Dynamic wavelet and equivalent models ⋮ Improved structure selection for nonlinear models based on term clustering ⋮ Model validation tests for multivariable nonlinear models including neural networks ⋮ Application of a general multi-model approach for identification of highly nonlinear processes-a case study ⋮ Identification and nonlinearity compensation of hysteresis using NARX models ⋮ The design of energy transfer filters for energy focus filtering ⋮ Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks ⋮ A suboptimal bootstrap method for structure detection of non-linear output-error models with application to human ankle dynamics ⋮ Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data ⋮ Identification of non-linear parametrically varying models using separable least squares ⋮ A new direct approach of computing multi-step ahead predictions for non-linear models ⋮ An iterative orthogonal forward regression algorithm ⋮ Online identification of the bilinear model expansion on Laguerre orthonormal bases ⋮ Non-linear system identification using wavelet multiresolution models

Cites Work

• Maximum-power validation of models without higher-order fitting
• Domain of stability of synchronous generators by a cell mapping approach
• A prediction-error and stepwise-regression estimation algorithm for non-linear systems
• Orthogonal parameter estimation algorithm for non-linear stochastic systems
• Identification of non-linear output-affine systems using an orthogonal least-squares algorithm
• Identification of non-linear rational systems using a prediction-error estimation algorithm
• On model structure testing in system identification
• Orthogonal least squares methods and their application to non-linear system identification
• Least Squares Computations by Givens Transformations Without Square Roots
• Solving linear least squares problems by Gram-Schmidt orthogonalization

This page was built for publication: Extended model set, global data and threshold model identification of severely non-linear systems