Novel genetic-based negative correlation learning for estimating soil temperature

Novel genetic-based negative correlation learning for estimating soil temperature

A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of c...

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Bibliographic Details
Main Authors: S. M. R. Kazemi, Behrouz Minaei Bidgoli, Shahaboddin Shamshirband, Seyed Mehdi Karimi, Mohammad Ali Ghorbani, Kwok-wing Chau, Reza Kazem Pour
Format: Article
Language:English
Published: Taylor & Francis Group 2018-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Daily soil temperature
neural network ensemble model
negative correlation learning
genetic algorithm
estimation
Online Access:http://dx.doi.org/10.1080/19942060.2018.1463871

Internet

http://dx.doi.org/10.1080/19942060.2018.1463871

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