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FAULT DIAGNOSIS IN AIR-HANDLING UNIT SYSTEM USING DYNAMIC FUZZY NEURAL NETWORK

机译:基于动态模糊神经网络的空气处理机组系统故障诊断

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摘要

In this paper, an efficient fault diagnosis method for Air-Handling Unit (AHU) using dynamic fuzzy neural networks (DFNNs) is presented. The proposed fault diagnosis method has the following salient features: (1) Structure identification and parameters estimation are performed automatically and simultaneously without partitioning the input space and selecting initial parameters a priori; (2) Fuzzy rules can be recruited or deleted dynamically; (3) Fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. Simulation results demonstrate that fast training and diagnosis speed and high diagnosis rate can be achieved.
机译:提出了一种基于动态模糊神经网络(DFNN)的空气处理机组故障诊断方法。提出的故障诊断方法具有以下几个显着特征:(1)自动识别识别参数并自动进行,无需划分输入空间和先验选择初始参数。 (2)模糊规则可以动态补充或删除; (3)无需求助于反向传播(BP)迭代学习即可快速生成模糊规则,这是许多现有方法所采用的通用方法。仿真结果表明,该方法可以达到训练速度快,诊断速度快,诊断率高的目的。

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