By Topic

Neural network based signal prediction and parameter estimation for underwater layered media systems identification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Setayeshi, S. ; Dept. of Electr. Eng., Tech. Univ. Nova Scotia, Halifax, NS, Canada ; El-Hawary, F.

A method for underwater layered media (UWLM) modeling accounting for attenuation effects is proposed. A simple nonlinear structure and an algorithm for implementing and simulating the model based on the behavior of the media is designed. The assumption of suitable parameters in the modeling permits us to design a more accurate model. A model that responds to the acoustical input is employed to test the artificial neural network's ability in signal prediction and parameter estimation. Experimental results from the implemented programs are presented. A feed-forward multi-layered neural network trained by the back-propagation algorithm using the min-max amplitude ranges of an output signal of the UWLM is designed and evaluated. This is based on a computer simulation of synthetic data, to estimate the parameters of the media including attenuation factors, reflection coefficients, travel times and decay parameters. Based on experiments estimating the parameters of the media and predicting its output signal, the network produces the same computational results as those of the original assumed media structure. The results of computer simulation of the underwater layered media modeling in conjunction with the proposed neural network training process are given. The results suggest that the proposed network can supplement, or replace conventional techniques for parameter estimation and output prediction in system identification. The method presented also offers advantages in speed and efficiency over existing estimates techniques

Published in:

Electrical and Computer Engineering, 1994. Conference Proceedings. 1994 Canadian Conference on

Date of Conference:

25-28 Sep 1994