基于粒子群改进神经网络的舰艇磁场推算模型

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

针对目前线性建模解决舰艇内外磁场推算问题时存在的困难,从非线性优化的角度出发,建立了内外磁场之间的误差反向传播神经网络预报模型.为了改善网络的固有缺陷,利用粒子群算法优化网络的初始权值与阈值,使其能够逃离局部最优点,增强了网络的鲁棒性.该方法避免了利用线性化方法存在的诸多困难,可实现舰艇内外磁场推算.利用船模实验对网络预测的准确性进行了验证,结果表明其换算精度较线性方法有所提高,满足工程实际需求.

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.