A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

DOI10.1016/j.apm.2011.09.066zbMath1252.62090OpenAlexW1999933124MaRDI QIDQ693377

Xue-Ping Dong, Hui Peng, Min Gan

Publication date: 7 December 2012

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2011.09.066

zbMATH Keywords

radial basis function networktime series modelinghybrid training approach

Mathematics Subject Classification ID

Related Items (3)

A variable projection method for the general radial basis function neural network ⋮ An optimization method for control parameters of underwater gliders considering energy consumption and motion accuracy ⋮ Data-driven modelling based on artificial neural networks for predicting energy and effluent quality indices and wastewater treatment plant optimization

Uses Software

• TDSL
• ANFIS
• Optimization Toolbox
• TSDL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Time series forecasting using a hybrid ARIMA and neural network model
• Identification of fuzzy models using a successive tuning method with a variant identification ratio
• Time series forecasting with a nonlinear model and the scatter search meta-heuristic
• Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• A simulation study of artificial neural networks for nonlinear time-series forecasting
• Time-series forecasting using GA-tuned radial basis functions
• Time series analysis using normalized PG-RBF network with regression weights
• A new look at the statistical model identification

This page was built for publication: A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction