基于模糊神经网络的水下机器人实时状态监测模型

本文提出了基于人工智能的自主式水下机器人推进器及舵状态监测模块结构,基于模糊神经网络技术,以模糊自适应学习控制网络为核心构筑了状态监测模型,提出了基于最大权值矩阵的规则提取及基于浮点数编码的遗传算法参数调整方法,分析了推进器及舵的状态监测过程,并基于监测模型探讨了推进器及舵工作状态的评价方法.通过计算机仿真试验,验证了本文所提方法的有效性和监测模型的可行性.

Real Time Condition Monitoring Model for Autonomous Underwater Vehicle Based on Fuzzy-Neural Networks

In this paper, the structure of condition monitoring module for pr opellers and rudder of autonomous underwater vehicle based on the artificial int elligence is presented. With the technology of fuzzy-neural networks and taking the fuzzy adaptive learning control network as kernel, authors construct the co ndition monitoring model, and introduce a method, which includes the rules extra ction based on the maximum weights matrix and the parameters amendment based on genetic algorithm by floating-point coding, and analyse the condition monitorin g process of propellers and rudder, and probe into the appraisement method of th e working condition for propellers and rudder. The result of the computer simula tion shows that the method proposed by authors is effective and the condition mo nitoring model is feasible.