An efficient self-organizing RBF neural network for water quality prediction

DOI10.1016/j.neunet.2011.04.006zbMath1263.92002OpenAlexW2042728365WikidataQ51564829 ScholiaQ51564829MaRDI QIDQ1952552

Qi-Li Chen, Jun-Fei Qiao, Hong-Gui Han

Publication date: 31 May 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2011.04.006

zbMATH Keywords

radial basis function (RBF)flexibility structurewater quality prediction

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Dynamical systems in biology (37N25) Neural networks for/in biological studies, artificial life and related topics (92B20) Ecology (92D40) Systems biology, networks (92C42)

Related Items (7)

Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach ⋮ A deep belief network with PLSR for nonlinear system modeling ⋮ Online sequential echo state network with sparse RLS algorithm for time series prediction ⋮ A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation ⋮ Radial basis function neural network based on an improved exponential decreasing inertia weight-particle swarm optimization algorithm for AQI prediction ⋮ Multiple harmonic-source classification using a self-organization feature map network with voltage-current wavelet transformation patterns ⋮ Efficient self-organizing multilayer neural network for nonlinear system modeling

Uses Software

• UCI-ml

Cites Work

• Identification and prediction using recurrent compensatory neuro-fuzzy systems
• Sensitivity analysis applied to the construction of radial basis function networks
• A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks

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