On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network

2021 ◽  
Vol 172 ◽  
pp. 121159 ◽  
Author(s):  
Mehdi Jamei ◽  
Ismail Adewale Olumegbon ◽  
Masoud Karbasi ◽  
Iman Ahmadianfar ◽  
Amin Asadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Hybrid Nanofluids

scholarly journals Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

2004 ◽  
Vol 4 (3) ◽  
pp. 3653-3667 ◽  
Author(s):  
D. J. Lary ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Learning Algorithm ◽  
Feed Forward ◽  
The Neural Network ◽  
Fortran Code ◽  
Feed Forward Neural Networks ◽  
Time Of Year

Abstract. In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

scholarly journals A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Feed Forward Neural Network ◽  
Feed Forward

Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran

2009 ◽  
Vol 57 (2) ◽  
pp. 271-293 ◽  
Author(s):  
Khalil Rezaei ◽  
Bernard Guest ◽  
Anke Friedrich ◽  
Farajollah Fayazi ◽  
Muhammad Nakhaei ◽  
...  
Keyword(s):  
Neural Network ◽  
Interpolation Function ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Subsurface Sediments ◽  
Function Models
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