摘要
针对目前数值建模解决舰艇内外磁场推算问题时存在的困难,从智能优化的角度出发,建立了内外磁场之间的径向基神经网络预报模型。该方法避免了利用数值建模存在的诸多困难,即可实现舰艇内外磁场有效推算,并利用船模实验验证了网络预测的准确性,其换算精度相较于数值建模有所提高,满足工程实际需求。
The magnetic anomaly created by ferromagnetic ships may endanger their invisibility. Nowadays, a new technique called closed-loop degaussing system can reduce the magnetic anomaly especialIy permanent one in real-time. To achieve it, a model able to predict off-board magnetic field from onboard measurements is required. Many researchers settle the problem by some numerical models. In this paper, a Radial Basis Function（RBF）neural network is proposed to solve it. The method can avoid many problems from linear model. Its high accuracy and good generalization ability have been tested by a mockup experiment.
出处
《舰船电子工程》
2013年第5期161-162,171,共3页
Ship Electronic Engineering
基金
国家海洋专项基金资助项目(420050101)资助
关键词
舰船
磁场
闭环消磁
径向基
神经网络
ship, magnetic field, closed loop degaussing, RBF, neural network
