基于径向基神经网络的舰艇空间磁场延拓

针对目前线性化方法解决舰船空间磁场之间推算时存在的困难,论文从智能优化的角度出发,建立了舰艇空间磁场之间的径向基神经网络预报模型.该方法避免了利用线性化方法存在的诸多困难,即可实现舰艇空间磁场的换算,并利用船模实验验证了网络预测的准确性,换算精度较高,满足工程实际需求.

Magnetic Anomaly Extrapolation of Submarines Based on Neural Network

Magnetic anomaly created by ferromagnetic ships may make them vulnerable to detections and mines. In order to reduce the anomaly, it is important to evaluate magnetic field firstly. Underwater field can be measured easily, but up per air field is hard to be got. To achieve it, a model able to predict upper air magnetic field from underwater measurements is required. In this paper, a Radial Basis Function（RBF） neural network model is built to solve it. The method can avoid many problems from linear model and its high accuracy and good robustness are tested by a mockup experiment.