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.

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