Application of a general multi-model approach for identification of highly nonlinear processes-a case study
From MaRDI portal
Publication:4036110
DOI10.1080/00207179308934380zbMath0825.93131OpenAlexW2168652372MaRDI QIDQ4036110
Dale E. Seborg, Martin Pottmann, Heinz Unbehauen
Publication date: 16 May 1993
Published in: International Journal of Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207179308934380
Related Items (13)
Identification of nonlinear system structure and parameters using regime decomposition ⋮ Comparison of two construction algorithms for local model networks ⋮ System impulse response identification using a multiresolution neural network ⋮ Some new trends in identification and modeling of nonlinear dynamical systems ⋮ A randomized algorithm for nonlinear model structure selection ⋮ Identification of a class of nonlinear continuous-time systems using Hartley modulating functions ⋮ A simplified NARMAX method using nonlinear input-output data ⋮ MIMO LPV state-space identification of open-flow irrigation canal systems ⋮ Identification of polynomial input/output recursive models with simulation error minimisation methods ⋮ Approximate models for nonlinear dynamical systems and their generalization properties ⋮ On Identifying Global Nonlinear Discrete Models from Chaotic Data ⋮ Unnamed Item ⋮ An identification algorithm for polynomial NARX models based on simulation error minimization
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Statistical predictor identification
- Structure identification of nonlinear dynamic systems - A survey on input/output approaches
- Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
- Application and comparison of different identification schemes under industrial conditions
- Selection of Regressors
- Persistent excitation in bilinear systems
- A Bayesian comparison of different classes of dynamic models using empirical data
- Extended model set, global data and threshold model identification of severely non-linear systems
This page was built for publication: Application of a general multi-model approach for identification of highly nonlinear processes-a case study
