Greenhouse temperature modeling: A comparison between sigmoid neural networks and hybrid models

DOI10.1016/J.MATCOM.2003.09.004zbMath1041.92043OpenAlexW1984366931MaRDI QIDQ1826534

Raphael Linker, Ido Seginer

Publication date: 6 August 2004

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2003.09.004

zbMATH Keywords

DatabaseRadial basis functionExtrapolationPrior knowledgeTraining domain

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Ecology (92D40)

Related Items (1)

Constructing neural network sediment estimation models using a data-driven algorithm

Cites Work

• Identification of time-varying nonlinear systems using minimal radial basis function neural networks

This page was built for publication: Greenhouse temperature modeling: A comparison between sigmoid neural networks and hybrid models