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

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.

[1]  Robert J. Marks,et al.  Inversion of feedforward neural networks: algorithms and applications , 1999, Proc. IEEE.

[2]  Andreas Antoniou,et al.  Geoacoustic inversion with artificial neural networks , 1999, Oceans '99. MTS/IEEE. Riding the Crest into the 21st Century. Conference and Exhibition. Conference Proceedings (IEEE Cat. No.99CH37008).

[3]  Michael B. Porter,et al.  Computational Ocean Acoustics , 1994 .

[4]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  S. Dosso.  Geoacoustic inversion and appraisal , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[6]  Mohamed A. El-Sharkawi,et al.  Environmentally Adaptive Sonar Control in a Tactical Setting , 2002 .

[7]  Robert J. Marks,et al.  Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks , 1999 .

[8]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).