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Wave tranquility studies using neural networks

DSpace at IIT Bombay

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Title Wave tranquility studies using neural networks
 
Creator LONDHE, SN
DEO, MC
 
Subject neural networks
learning systems
mathematical models
information analysis
 
Description Information on heights of waves and their distribution around harbor entrances is traditionally obtained from the knowledge of incident wave, seabed and harbor characteristics by using experimental as well as numerical models. This paper presents an alternative to these techniques based on the computational tool of neural networks. Modular networks were developed in order to estimate wave heights in and around a dredged approach channel leading to harbor entrance. The data involved pertained to two harbor locations in India. The training of networks was done using a numerical model, which solved the mild slope equation. Test of the network with several alternative error criteria confirmed capability of the neural network approach to perform the wave tranquility studies. A variety of learning schemes and search routines were employed so as to select the best possible training to the network. Mutual comparison between these showed that the scaled conjugate method was the fastest among all whereas the one step secant scheme was the most memory efficient. The Brent's search and the golden section search routines forming part of the conjugate gradient Fletcher–Reeves update approach of training took the least amount of time to train the network per epoch. Calibration of the neural network with both mean square as well as the sum squared error as performance functions yielded satisfactory results.
 
Publisher Elsevier
 
Date 2009-03-23T05:11:11Z
2011-11-25T20:01:55Z
2011-12-26T13:07:43Z
2011-12-27T05:55:45Z
2009-03-23T05:11:11Z
2011-11-25T20:01:55Z
2011-12-26T13:07:43Z
2011-12-27T05:55:45Z
2003
 
Type Article
 
Identifier Marine Structures 16(6), 419-436
0951-8339
http://dx.doi.org/10.1016/j.marstruc.2003.09.001
http://hdl.handle.net/10054/1051
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1051
 
Language en