An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

Abstract In this study, a comparative analysis is done to evaluate the ability of classified radial basis function neural network (CRBFNN) model in estimation of flow variables in sharp open-channel bends with bend angles of 60° and 90°. Accordingly, a RBFNN model is integrated with classification m...

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Bibliographic Details
Main Authors: Azadeh Gholami, Hossein Bonakdari, Amir Hossein Zaji, Ali Akbar Akhtari
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Applied Water Science
Subjects:
Classified model
Radial basis function neural network
60° Bend
90° Bend
Flow velocity
Flow depth
Online Access:http://link.springer.com/article/10.1007/s13201-019-1020-y

Internet

http://link.springer.com/article/10.1007/s13201-019-1020-y

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