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Modeling based on Elman Wavelet Neural Network for Class-D Power Amplifiers |
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PP: 2445-2453 |
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Author(s) |
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Li Wang,
Jie Shao,
Yaqin Zhong,
Weisong Zhao,
Reza Malekian,
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Abstract |
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In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into
power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in
the PS-IMDs. In this paper, a new behavioral modeling based on the Elman Wavelet Neural Network (EWNN) is proposed to study the
nonlinear distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are employed as the activation function and there
is a normalized operation in the hidden layer, the modification of the scale factor and translation factor in the wavelet functions are
ignored to avoid the fluctuations of the error curves. When there are 30 neurons in the hidden layer, to achieve the same square sum
error (SSE) emin = 10−3, EWNN needs 31 iteration steps, while the basic Elman neural network (BENN) model needs 86 steps. The
Volterra-Laguerre model has 605 parameters to be estimated but still can’t achieve the same magnitude accuracy of EWNN. Simulation
results show that the proposed approach of EWNN model has fewer parameters and higher accuracy than the Volterra-Laguerre model
and its convergence rate is much faster than the BENN model. |
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