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    ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 >  Item 310904400/18712

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/18712

    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 13:57:28 (UTC+8)
    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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