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Author Asgary, R. ♦ Mohammadi, K.
Sponsorship World Federation on Soft Comput. ♦ European Soc. for Fuzzy Logic and Technol. ♦ European Neural Network Soc. ♦ The Warsaw School of Social Psychology (SWPS) ♦ Polish Minist. of Sci. Res. and Inf. Technol
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Micromechanical devices ♦ Electrical engineering ♦ Intelligent networks ♦ Fault detection ♦ Neural networks ♦ Fuzzy neural networks ♦ Electrical fault detection ♦ Robustness ♦ Pattern recognition ♦ Kernel
Abstract 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.
Description Author affiliation: Electr. Eng. Dept., Iran Univ. of Sci. & Technol., Tehran, Iran (Asgary, R.; Mohammadi, K.)
ISBN 0769522866
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-09-08
Publisher Place Poland
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 189.85 kB
Page Count 5
Starting Page 136
Ending Page 140


Source: IEEE Xplore Digital Library