Regularized orthogonal least squares algorithm for constructing radial basis function networks

DOI10.1080/00207179608921659zbMath0856.68120OpenAlexW2119739937MaRDI QIDQ4894037

No author found.

Publication date: 3 March 1997

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

Full work available at URL: https://eprints.soton.ac.uk/250999/1/771371879_content.pdf

zbMATH Keywords

radial basis function networksregularized orthogonal least squares learning algorithm

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items

Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms, Multi-objective optimization for bus body with strength and rollover safety constraints based on surrogate models, Using radial basis function networks for function approximation and classification, Dynamics model abstraction scheme using radial basis functions, Soft computing based techniques for short-term load forecasting, Combining biometric fractal pattern and particle swarm optimization-based classifier for fingerprint recognition, Sparse multinomial kernel discriminant analysis (sMKDA), Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach, Incremental kernel minimum squared error (KMSE), PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH OLS ALGORITHM-BASED IMAGE CLASSIFICATION, Neuro-control of unmanned underwater vehicles, Locally regularised two-stage learning algorithm for RBF network centre selection

Uses Software

• ASMOD

Cites Work

• Threshold models in non-linear time series analysis
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• ASMO—Dan algorithm for adaptive spline modelling of observation data
• Modelling and analysis of non-linear time series
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Ridge Regression: Biased Estimation for Nonorthogonal Problems