模糊概率神经网络在MEMS故障检测中的应用

R. Asgary, K. Mohammadi
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引用次数: 5

摘要

在电子和计算机系统中,检测数字故障有不同的方法。但对于模拟故障,存在一些问题。这类故障由许多不同的参数故障组成,数字故障检测方法无法检测到。神经网络是目前提出的模拟故障检测方法之一。故障检测实际上是一种模式识别任务。故障数据和无故障数据是必须识别的不同模式。本文将概率神经网络应用于微机电系统的故障检测。采用模糊系统来提高网络的性能。最后对不同的网络结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using fuzzy probabilistic neural network for fault detection in MEMS
There are different methods for detecting digital faults in electronic and computer systems. But for analog faults, there are some problems. This kind of faults consists of many different and parametric faults, which can not be detected by digital fault detection methods. One of the proposed methods for analog fault detection is neural networks. Fault detection is actually a pattern recognition task. Faulty and fault free data are different patterns which must be recognized. In this paper we use a probabilistic neural network for fault detection in MEMS. A fuzzy system is used to improve performance of the network. Finally different network results are compared.
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