A hybrid approach with artificial neural networks, Levenberg–Marquardt and simulated annealing methods for the solution of gas–liquid adsorption inverse problems

2009 ◽  
Vol 17 (1) ◽  
pp. 85-96 ◽  
Author(s):  
Jader Lugon ◽  
Antônio J. Silva Neto ◽  
César Costapinto Santana
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Inverse Problems ◽  
Hybrid Approach ◽  
Liquid Adsorption ◽  
Levenberg Marquardt ◽  
Artificial Neural

Simulation of the Mean-Zero-Up-Crossing Wave Period Using Artificial Neural Networks Trained With Simulated Annealing Algorithm

2006 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
F. Aryana ◽  
S. F. Yasseri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
North East ◽  
The North ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Annealing Algorithm ◽  
Artificial Neural

The aim of this work is to simulate the 3-houly mean zero-up-crossing wave periods (Tzs) of the sea-states of a future period for a location in the North East Pacific. Seven multi-layer artificial neural networks (ANNs) were trained with simulated annealing algorithm. The output of each ANN was used for estimating each of the 7 parameters of a new distribution, described in Appendix A, called hepta-parameter spline proposed for the conditional distribution of the Tz given some significant wave heights and mean zero-up-crossing wave periods. After estimating the parameters of the conditional distributions, the Tzs have been forecasted from the hepta-parameter spline distributions corresponding to the Tzs of the period.

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