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Inversion of neural network underwater acoustic model for estimation of bottom parameters using modified particle swarm optimizers

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5 Author(s)
Thompson, B.B. ; Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA ; Marks, R.J., II ; El-Sharkawi, M.A. ; Fox, W.J.
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Given a complicated and computationally intensive underwater acoustic model in which some acoustic measurement is a function of sonar system and environmental parameters, it is computationally beneficial to train a neural network to emulate the properties of that model. Given this neural network model, we now have a convenient means of performing geoacoustic inversion without the computational intensity required when attempting to do so with the actual model. This paper proposes an efficient and reliable method of performing the inversion of a neural network underwater acoustic model to obtain parameters pertaining to the characteristics of the ocean floor, using two different modified version of particle swarm optimization (PSO): two-step (gradient approximation) PSO and hierarchical cluster-based PSO.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:2 )

Date of Conference:

20-24 July 2003