| 基于RBF神经网络的水下机器人传感器状态监测方法研究 |
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| 引用本文： | 张铭钧,孙瑞琛,王玉甲. 基于RBF神经网络的水下机器人传感器状态监测方法研究[J]. 哈尔滨工程大学学报, 2005, 26(6): 726-731 |
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| 作者姓名： | 张铭钧 孙瑞琛 王玉甲 |
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| 作者单位： | 哈尔滨工程大学,机电工程学院,黑龙江,哈尔滨,150001;哈尔滨工程大学,机电工程学院,黑龙江,哈尔滨,150001;哈尔滨工程大学,机电工程学院,黑龙江,哈尔滨,150001 |
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| 摘 要： | 为了实现水下机器人多传感器状态监测,根据其工作环境及所配置传感器的数量,提出了基于径向基函数(RBF)神经网络的传感器状态监测方法,建立了二级神经网络监测模型,解决了多传感器故障诊断和信号恢复的问题.基于某型水下机器人海中试验数据进行计算机仿真试验的结果,验证了该方法的有效性和可行性.
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| 关 键 词： | 水下机器人传感器 RBF神经网络 状态监测 数据融合 |
| 文章编号： | 1006-7043(2005)06-0726-06 |
| 收稿时间： | 2005-04-03 |
| 修稿时间： | 2005-04-03 |
Research on condition monitor for AUV sensors based on RBF neural network |
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| ZHANG Ming-jun,SUN Rui-chen,WANG Yu-jia. Research on condition monitor for AUV sensors based on RBF neural network[J]. Journal of Harbin Engineering University, 2005, 26(6): 726-731 |
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| Authors: | ZHANG Ming-jun SUN Rui-chen WANG Yu-jia |
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| Affiliation: | School of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China |
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| Abstract: | To overcome the problem of multi-sensor condition monitoring of an autonomous underwater vehicle(AUV),a method based on a radial basis function(RBF) neural network was proposed and a two-level neural networks model was constructed.The problem of failure diagnosis and signal restoration for an AUV multi-sensor was solved.Computer simulations using experimental data from an AUV show that the proposed condition monitoring model is feasible. |
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| Keywords: | sensor of autonomous underwater vehicles(AUV) radial basis function (RBF) neural network condition monitoring |
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