Locally regularised two-stage learning algorithm for RBF network centre selection
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Publication:5497406
DOI10.1080/00207721.2010.545490zbMath1307.93372OpenAlexW1963989191MaRDI QIDQ5497406
Kang Li, Jing Deng, George W. Irwin
Publication date: 4 February 2015
Published in: International Journal of Systems Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207721.2010.545490
Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Stochastic systems in control theory (general) (93E03)
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A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems ⋮ Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles ⋮ Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults
Cites Work
- A two-stage algorithm for identification of nonlinear dynamic systems
- Model selection approaches for non-linear system identification: a review
- Neural networks for nonlinear dynamic system modelling and identification
- Orthogonal least squares methods and their application to non-linear system identification
- 10.1162/15324430152748236
- Mean-tracking clustering algorithm for radial basis function centre selection
- Algorithms for minimal model structure detection in nonlinear dynamic system identification
- Givens rotation based fast backward elimination algorithm for RBF neural network pruning
- Regularized orthogonal least squares algorithm for constructing radial basis function networks
- General model-set design methods for multiple-model approach
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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