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研究目的:为消除传统隧道监测维护中低频率、长周期、高费用等缺点,适应隧道即时监测护养的新兴要求,本文通过对上海地铁变形预测研究,提出一种基于多智能体的隧道变形在线监测系统。研究结论:(1)在监测系统中提出一种基于分层组件结构设计的通用Agent模型,该模型包括客户感知层、智能业务层与服务效应层,并以粗细两种化度归纳描述智能体内部结构;(2)依据Agent通用模型及隧道变形监测自身特点搭建具有6层复合结构特点的隧道变形监测系统,提出基于熟人协作沟通机制,实现系统的松耦合和低负载构造;(3)利用上海地铁变形实测数据验证预测模块Agent的工作效能,实验表明该系统具有较强的自治性、合作性,并具有一定的实用价值,对隧道监测系统的搭建有一定的指导意义。 相似文献
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A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application. 相似文献
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