scholarly journals Artificial neural networks as empirical models for estimating phytoplankton production

1996 ◽  
Vol 139 ◽  
pp. 289-299 ◽  
Author(s):  
M Scardi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Phytoplankton Production ◽  
Artificial Neural

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

2015 ◽  
Author(s):  
Ehsan Sarshari ◽  
Philippe Mullhaupt
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Local Scour ◽  
Bridge Piers ◽  
Local Conditions ◽  
The Neural Network ◽  
Artificial Neural

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

scholarly journals A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz

2017 ◽  
Vol 16 (3) ◽  
pp. 708-722 ◽  
Author(s):  
Bruno J. Cavalcanti ◽  
Gustavo A. Cavalcante ◽  
Laércio M. de Mendonça ◽  
Gabriel M. Cantanhede ◽  
Marcelo M.M. de Oliveira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Path Loss ◽  
Empirical Models ◽  
Model Based ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural

scholarly journals An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211445 ◽  
Author(s):  
Simone Franceschini ◽  
Lorenzo Tancioni ◽  
Massimo Lorenzoni ◽  
Francesco Mattei ◽  
Michele Scardi
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document