TY - JOUR
T1 - Artificial Neural Network Based System Identification of an Irrigation Main Canal Pool
AU - Hernandez Lopez, Ybrain
AU - Feliu Batlle, Vicente
AU - Rivas Perez, Raul
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - In this paper by applying system identification tools a neural network model of an irrigation main canal pool is obtained. The complete system identification procedure, from experimental design to model validation, taking into account prior physical information, is developed. It is established that a nonlinear model with NARX structure can adequately describe the dynamic behavior of an irrigation main canal pool. The model validation results show that the model obtained reproduces with high accuracy the observed data and therefore it can be applied in the design of nonlinear control systems and/or for prediction purposes.
AB - In this paper by applying system identification tools a neural network model of an irrigation main canal pool is obtained. The complete system identification procedure, from experimental design to model validation, taking into account prior physical information, is developed. It is established that a nonlinear model with NARX structure can adequately describe the dynamic behavior of an irrigation main canal pool. The model validation results show that the model obtained reproduces with high accuracy the observed data and therefore it can be applied in the design of nonlinear control systems and/or for prediction purposes.
KW - Artificial neural network
KW - irrigation main canal pool
KW - irrigation system automation
KW - management of water resources
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=85028732173&partnerID=8YFLogxK
U2 - 10.1109/TLA.2017.8015040
DO - 10.1109/TLA.2017.8015040
M3 - Article
AN - SCOPUS:85028732173
SN - 1548-0992
VL - 15
SP - 1595
EP - 1600
JO - IEEE Latin America Transactions
JF - IEEE Latin America Transactions
IS - 9
M1 - 8015040
ER -