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Journal articles
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.
Cited literature [14 references]
https://hal-centralesupelec.archives-ouvertes.fr/hal-02397943
Contributor : Morgan Roger <>
Submitted on : Friday, December 6, 2019 - 5:45:30 PM
Last modification on : Thursday, September 17, 2020 - 12:26:01 PM
Long-term archiving on: : Saturday, March 7, 2020 - 5:11:53 PM
Contributor : Morgan Roger <>
Submitted on : Friday, December 6, 2019 - 5:45:30 PM
Last modification on : Thursday, September 17, 2020 - 12:26:01 PM
Long-term archiving on: : Saturday, March 7, 2020 - 5:11:53 PM
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MWCL - Hyperparameter Optimiza...
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