Neural networks for nonlinear dynamic system modelling and identification

DOI10.1080/00207179208934317zbMath0764.93021OpenAlexW2149350706MaRDI QIDQ4020049

Sheng Chen, Stephen A. Billings

Publication date: 16 January 1993

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

Full work available at URL: http://eprints.whiterose.ac.uk/78723/1/acse%20research%20report%20436.....pdf

zbMATH Keywords

neural networksnonlinear systems identificationnonlinear discrete-time dynamic systems

Mathematics Subject Classification ID

Related Items (33)

Rational wavelets in Wiener-like modeling ⋮ A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system ⋮ Identification of non-linear time series via kernels ⋮ Simple recursive algorithm for linear-in-the-parameters nonlinear model identification ⋮ Modelling of river discharges and rainfall using radial basis function networks based on support vector regression ⋮ Recursive prediction error identification and scaling of non-linear state space models using a restricted Black box parameterization ⋮ Nonlinear black-box modeling in system identification: A unified overview ⋮ Identification of nonlinear discrete-time systems using raised-cosine radial basis function networks ⋮ A multilayer recurrent neural network for on-line synthesis of minimum-norm linear feedback control systems via pole assignment ⋮ Nonlinear identification of unsteady heat transfer of a cylinder in pulsating cross flow ⋮ Generalized multiscale radial basis function networks ⋮ New criteria for dissipativity analysis of fractional-order static neural networks ⋮ Analytical fuzzy predictive control applied to wastewater treatment biological processes ⋮ System identification using autoregressive Bayesian neural networks with nonparametric noise models ⋮ A robust model structure selection method for small sample size and multiple datasets problems ⋮ Identification and control of nonlinear system based on Laguerre-ELM Wiener model ⋮ Hierarchical data organization, clustering and denoising via localized diffusion folders ⋮ SpectralCAT: categorical spectral clustering of numerical and nominal data ⋮ MTN optimal tracking control of SISO nonlinear time-varying discrete-time systems without mechanism models ⋮ Physics-informed multi-LSTM networks for metamodeling of nonlinear structures ⋮ A simple flexible and robust control strategy for wind energy conversion systems connected to a utility grid ⋮ Semiglobal stabilization of nonlinear systems using fuzzy control and singular perturbation methods ⋮ Deadzone compensation based on constrained RBF neural network ⋮ Nonlinear systems parameter estimation using neural networks: Application to synchronous machines ⋮ A Measurement Fusion Method for Nonlinear System Identification Using a Cooperative Learning Algorithm ⋮ Nonlinear system identification via direct weight optimization ⋮ About extracting dynamic information of unknown complex systems by neural networks ⋮ Stable sequential identification of continuous nonlinear dynamical systems by growing radial basis function networks ⋮ Maximum power extraction on wind turbine systems using block‐backstepping with gradient dynamics control ⋮ Locally regularised two-stage learning algorithm for RBF network centre selection ⋮ A neural network assisted computed torque method for manipulator tracking control problems ⋮ Fuzzy Sets and Systems. Author index volume 129 (2002) ⋮ Truncated Chebyshev series approximation of fuzzy systems for control and nonlinear system identification

Cites Work

• Self-organization and associative memory
• Dynamic system identification. Experiment design and data analysis
• Non-linear system identification using neural networks
• Practical identification of NARMAX models using radial basis functions
• Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
• Improved least squares identification
• Modified least squares algorithm incorporating exponential resetting and forgetting
• Identification of non-linear rational systems using a prediction-error estimation algorithm
• STATE-DEPENDENT MODELS: A GENERAL APPROACH TO NON-LINEAR TIME SERIES ANALYSIS
• Orthogonal least squares methods and their application to non-linear system identification
• Solving linear least squares problems by Gram-Schmidt orthogonalization
• Approximation by superpositions of a sigmoidal function
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Neural networks for nonlinear dynamic system modelling and identification