Comparative study of reference evapotranspiration estimation methods including Artificial Neural Network for dry sub-humid agro-ecological region

2016 ◽  
Vol 15 (3) ◽  
pp. 233 ◽  
Author(s):  
Saswat Kumar Kar ◽  
A.K. Nema ◽  
Abhishek Singh ◽  
B.L. Sinha ◽  
C.D. Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Reference Evapotranspiration ◽  
Estimation Methods ◽  
Ecological Region ◽  
Artificial Neural ◽  
Evapotranspiration Estimation

scholarly journals Comparative Study of Artificial Neural Network Based Channel Equalization Methods for MmWave Communications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Noe M. Yungaicela-Naula ◽  
Leyre Azpilicueta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Channel Equalization ◽  
Artificial Neural

scholarly journals An artificial neural network model for predicting characteristic input parameters for physics based modelling of drying process

2019 ◽  
Author(s):  
Ankita Sinha ◽  
Atul Bhargav
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Estimation Methods ◽  
Drying Process ◽  
Many Instruments ◽  
Input Parameters ◽  
Artificial Neural

Drying is crucial in the quality preservation of food materials. Physics-based models are effective tools to optimally control the drying process. However, these models require accurate thermo-physical properties; unavailability or uncertainty in the values of these properties increases the possibility of error. Property estimation methods are not standardized, and usually involve the use of many instruments and are time-consuming. In this work, we have developed an experimentally validated deep learning-based artificial neural network model that estimates sensitive input parameters of food materials using temperature and moisture data from a set of simple experiments. This model predicts input parameters with error less than 1%. Further, using input parameters, physics-based model predicts temperature and moisture to within 5% accuracy of experiments. The proposed work when interfaced with food machinery could play a significant role in process optimization in food processing industries.

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