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The shaft-rate electric field controlling method based on RBF neural network

机译:基于RBF神经网络的轴速电场控制方法

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The shaft-rate(SR) electric field is an important target of the ship at a very low frequency in the marine environment. To protect our ships from being attacked by SR electric field fuze weapon or detected by other equipments, the signal characteristic must be controlled. Based on analysis of SR electric field, an effectual way of weakening the SR electric field signal is presented. First, a RBF neural network prediction model is set up, after values predicted by the model are obtained, a reverse current whose magnitude is the same as the predicting value is exported to weaken the SR electric field signal. The simulation result shows that the method could control the signal characteristic effectively.
机译:轴速(SR)电场是船舶在海洋环境中处于非常低频率的重要目标。为了保护我们的船只免受SR电场引信武器的攻击或被其他设备检测到,必须控制信号特性。在分析SR电场的基础上,提出了一种削弱SR电场信号的有效方法。首先,建立RBF神经网络预测模型,获得模型预测的值后,输出大小与预测值相同的反向电流,以减弱SR电场信号。仿真结果表明,该方法可以有效地控制信号特性。

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