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Hyperparameter Optimization of Two-Hidden-Layer Neural Networks for Power Amplifiers Behavioral Modeling Using Genetic Algorithms

Abstract : Neural networks (NN) are efficient techniques for behavioral modeling of power amplifiers (PA). This paper proposes a genetic algorithm to determine the optimal hyperparam-eters of the NN model for a PA. Different activation functions are compared. The necessary number of training epochs is also studied to get an optimal solution with a significantly reduced computational complexity. Experimental measurements on a PA with different signals validate the NN models determined by the proposed method.
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Contributor : Morgan Roger <>
Submitted on : Friday, December 6, 2019 - 5:45:30 PM
Last modification on : Monday, March 30, 2020 - 4:52:10 PM
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Siqi Wang, Morgan Roger, Julien Sarrazin, Caroline Lelandais-Perrault. Hyperparameter Optimization of Two-Hidden-Layer Neural Networks for Power Amplifiers Behavioral Modeling Using Genetic Algorithms. IEEE Microwave and Wireless Components Letters, Institute of Electrical and Electronics Engineers, 2019, 29 (12), pp.802-805. ⟨10.1109/LMWC.2019.2950801⟩. ⟨hal-02397943⟩

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