A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers

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Title:	A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers
Authors:	陳永欽;Chen, Yeong-Chin
Contributors:	資訊工程學系
Keywords:	Machine learning;Intelligence method;Mega-fuzzification;Acoustical;Langevin;Piezoelectric transducer
Date:	2011-07
Issue Date:	2012-11-26 05:57:28 (UTC+0)
Abstract:	A Langevin piezoelectric transducer is used as a physical element for transmitting and receiving sound waves. The operating frequency of a transducer determines the distance that the sound wave can travel, so it is important to measure it. Due to the fact the structure of a transducer is quite complicated, it is quite difficult to estimate the precise physical parameters for the simulation model. Therefore, it takes a long time to measure the resonance frequency in the laboratory and fix the parameters by trial and error methods. This study applies a learning method to estimate a transducer frequency instead by trial and error experiments. The learning methods applied and compared including artificial neural network, support vector machine, C4.5, neuro-fuzzy, and ega-fuzzification. Compared with the theoretical one-dimensional model (simple lump element model), the results indicate that a learning method is an efficient way to estimate the piezoelectric transducer resonance frequency. The mega-fuzzification method is the best compared with other methods in this study.
Relation:	APPLIED MATHEMATICAL MODELLING;35(7):3334–3344
Appears in Collections:	[資訊工程學系] 期刊論文

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