The Model of Ship’s Magnetic Field Extrapolation Based on Neural Network Improved by Particle Swarm Optimization

Abstract

Abstract: The magnetic anomaly created by ferromagnetic submarines may endanger their invisibility. Nowadays, a new technique called closedloop degaussing system can reduce the magnetic anomaly especially permanent one in realtime. To achieve it, a model which is able to predict offboard magnetic field from on board measurements was required. Many researchers settle the problem by a linear model. A back propagation neural network model was proposed to solve it. The model can escape local optimum thanks to optimizing the initial weight values and threshold values by particle swarm optimization algorithm. The method can avoid many problems from linear model and its high accuracy and good robustness was tested by a mockup experiment.

Key words: ship, magnetic field, closed loop degaussing, particle swarm optimizer, error back propagation

CLC Number:

TM153.1

LIAN Li-Ting, XIAO Chang-Han, YANG Ming-Ming, ZHOU Guo-Hua. The Model of Ship’s Magnetic Field Extrapolation Based on Neural Network Improved by Particle Swarm Optimization[J]. Journal of Shanghai Jiaotong University, 2011, 45(06): 809-813.

References

［1］Oliver C, Jeanlouis C, Jeant B, et al. Recent improvements for solving inverse magneto static problem applied to thin shells［J］. IEEE Transactions and Magnetics, 2002, 38(2): 10051008.

［2］Carl S. Closedloop multisensor control system and method［P］. US Patent： 5189590, 19930223.

［3］Richard M, Robert A. Ship degaussing system and algorithm［P］. US Patent： 6965505 B1, 20051115.

［4］Vuillerunet Y. Closed loop degaussing applied to double hull submarine magnetization identification from near magnetic fields measurements［D］. Grenoble： Grenoble Electrical Engineering Laboratory, 2008: 2530.

［5］王鲸，刘胜道，苏广东．一种潜艇内外磁场换算算法的实验验证［J］．海军工程大学学报，2010, 22(1)：8386.

WANG Jing, LIU Shengdao, SU Guangdong. An experimental verification of a conversion of magnetic field inside and outside submarine［J］. Journal of Navy University of Engineering, 2010, 22(1): 8386.

［6］连丽婷，肖昌汉，刘胜道,等．基于粒子群算法的薄钢板两侧磁场推算中的位置优化［J］．上海交通大学学报， 2010， 44(7)： 975979.

LIAN Liting, XIAO Changhan, LIU Shengdao， et al. The measurement positions optimization in the extrapolation of steel’s magnetic field based on particle swarm optimization algorithm［J］. Journal of Shanghai Jiaotong University, 2010, 44(7): 975979.

［7］董长虹．Matlab神经网络与应用［M］．北京：国防工业出版社， 2007： 64110.

［8］Kennedy J, Eberhart R C. Particle swarm optimization ［C］// Proceedings of the IEEE International Conference on Neural Network. Perth, Australia: IEEE, 1995: 3943.

［9］WANG Ping, HUANG Zhenyi, ZHANG Mingya, et al. Mechanical property prediction of strip model based on PSOBP neural network［J］. Journal of Iron and Steel Research, 2007, 15(3): 8791.

［10］史东生，弟宇鸣，周春林．粒子群优化算法在神经网络识别γ能谱中的应用［J］．核技术，2007，30(7)：615619.

SHI Dongsheng, DI Yuming, ZHOU Chunlin. Application of particle swarm optimization to identify gamma spectrum with neural network［J］. Nuclear Techniques, 2007, 30(7): 615619.

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