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The Evaluation of Resonance Frequency for Piezoelectric Transducers by Machine Learning Methods

机译:机器学习方法评价压电传感器的共振频率

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Piezoelectric transducers are used in the application of sending and receiving sound waves. The distance at which the sound waves can be transmitted is determined by the transducer frequency. Therefore, measuring the frequency becomes an important issue. However, a large number of experiments are needed in the laboratory to simulate and measure the resonant frequency of the transducer. To simplify this problem, this study uses machine learning methods rather than laboratory experiments to estimate the frequency of transducers. The proposed method is compared with other methods, such as an artificial neural network, support vector machine, neuro-fuzzy, and mega-fuzzification. The results show that machine learning methods are efficient ways to assess the resonance frequency of a piezoelectric transducer, and mega-fuzzification method has the best accuracy among the comparative methods in this case.
机译:压电传感器用于发送和接收声波的应用。可以通过换能器频率来发送声波的距离。因此,测量频率成为一个重要问题。然而,在实验室中需要大量实验来模拟和测量换能器的谐振频率。为了简化这个问题,本研究使用机器学习方法而不是实验室实验来估计换能器的频率。将所提出的方法与其他方法进行比较,例如人工神经网络,支持向量机,神经模糊和Mega-fuzzification。结果表明,机器学习方法是评估压电换能器的共振频率的有效方法,而Mega-Fuzzifice方法在这种情况下具有最佳精度。

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