Ship Recognition Based on Magnetic Field and Improved Back Propagation Neutral Network

DOI

10.2991/iiicec-15.2015.340How to use a DOI?

Keywords

object recognition; magnetic field; Back Propagation neural network; Particle Swarm Optimization

Abstract

It is important and difficult for underwater weapons to get ship’s structure parameters from limited physical field signals. The degree of recognition will directly affect weapons’ attacking result. Nowadays, some researchers used acoustic model to recognize underwater object, however, few research about ascertaining these parameters such as object’s length, width and tonnage have been found. In this paper, we proposed an improved Back Propagation (BP) neural network model that can escape local optimum thanks to optimizing the initial weight values and threshold values by Particle Swarm Optimization (PSO) algorithm to solve it. The method can study the relationship between the positions and values of magnetic field curve’s extremums and structure parameters directly. Its high accuracy and good robustness have been validated by a test.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li-ting Lian
AU  - Ming-ming Yang
AU  - Long-long Zhao
PY  - 2015/03
DA  - 2015/03
TI  - Ship Recognition Based on Magnetic Field and Improved Back Propagation Neutral Network
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 1539
EP  - 1542
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.340
DO  - 10.2991/iiicec-15.2015.340
ID  - Lian2015/03
ER  -