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Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

机译:水下机器人系统状态监测的模糊神经网络模型研究

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摘要

A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.
机译:提出了一种用于系统状态监测的模糊神经网络结构等效模型,其输出为系统某些部分的状态或故障程度。该网络由六层神经元组成,分别代表隶属函数,模糊规则和输出。通过处理离线学习获得结构参数和权重,并从经验中得出模糊规则。基于该模糊神经网络的水下航行器状态自动监测计算机仿真结果表明,该网络在获取两个主螺旋桨的状态信息或故障程度方面是高效可行的。

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