| 基于神经网络的船舶轴频电场特征控制实验 |
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| 引用本文： | 谭浩,龚沈光,贾利.基于神经网络的船舶轴频电场特征控制实验[J].华中科技大学学报(自然科学版),2012,40(4):84-87. |
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| 作者姓名： | 谭浩 龚沈光 贾利 |
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| 作者单位： | 1. 海军工程大学兵器工程系,湖北武汉,430033
2. 海军91980部队,山东烟台,264000 |
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| 基金项目： | 海军工程大学水中军用目标国防重点实验室基金资助项目 |
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| 摘 要： | 任分析实验室船模轴频电场和海洋实测船舶轴频电场信号与环境电场数据的基础上，利用径向基神经网络预测模型对轴频电场信号进行控制．先对信号进行测量，在合理设置训练集与目标集的基础上建立预测模型，得到预测值后在下一时刻利用电极在场源处反向输出该预测值，以达到减弱特征信号的目的．通过对实验室与海洋实测船舶轴频电场数据进行处理，结果表明：用该方法能对船舶轴频电场信号特征进行有效控制，使幅值减小到原信号的30％以下．
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| 关 键 词： | 轴频电场 径向基神经网络 信号预测 时间序列预测 预测模型 信号特征控制 |
Controlling shaft-rate electric field using RBF neural networks |
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| Tan Hao,Gong Shenguang,Jia Li.Controlling shaft-rate electric field using RBF neural networks[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2012,40(4):84-87. |
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| Authors: | Tan Hao Gong Shenguang Jia Li |
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| Institution: | 1 Department of Weaponry Engineering,Naval University of Engineering,Wuhan 430033,China;2 Navy NO.91980,Yantai 264000,Shandong China) |
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| Abstract: | Based on fully analyzing of shaft-rate(SR) electric field and noise got in the lab and at sea,a method of controlling SR electric field through radical-basis-function(RBF) neural network was presented.The signal was got first to form the predicting model as the training data and the target data was set properly.The same value of the prediction result was exported reversely from the source using electrodes after it was obtained so as to weaken the characteristic signal.Through processing the SR electric field and noise got in the lab and at sea,it can be seen that this method can effectively control the SR signal of ship and can reduce the signal amplitude to less than 30%. |
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| Keywords: | SR electric field RBF neural network signal prediction time series prediction predic tion model signal controlling |
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