کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1704735 | 1012414 | 2011 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers](/preview/png/1704735.png)
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.
Journal: Applied Mathematical Modelling - Volume 35, Issue 7, July 2011, Pages 3334–3344