Modelling of river discharges and rainfall using radial basis function networks based on support vector regression

DOI10.1080/00207720310001640241zbMath1057.93500OpenAlexW2058388441MaRDI QIDQ4809248

No author found.

Publication date: 24 August 2004

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207720310001640241

zbMATH Keywords

nonlinear systemsneural networkradial basis functionsassociative memory networkssupport vector machineFuji river

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

A time-sequence-based fuzzy support vector machine adaptive filter for tremor cancelling for microsurgery

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multilayer feedforward networks are universal approximators
• Adaptive Modelling, Estimation and Fusion from Data
• Non-linear systems identification using radial basis functions
• Neural networks for nonlinear dynamic system modelling and identification
• Mean-tracking clustering algorithm for radial basis function centre selection
• Givens rotation based fast backward elimination algorithm for RBF neural network pruning
• Sliding mode state observers for discrete-time linear systems
• Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms

This page was built for publication: Modelling of river discharges and rainfall using radial basis function networks based on support vector regression