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Evolutionary extreme learning machine for the behavioral modeling of RF power amplifiers
Author(s) -
Fu Haipeng,
Wang Kaikai,
Ma Kaixue,
Xing Guangyu
Publication year - 2019
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2659
Subject(s) - extreme learning machine , generalization , amplifier , computer science , behavioral modeling , artificial intelligence , differential evolution , power (physics) , class (philosophy) , machine learning , artificial neural network , mathematics , telecommunications , physics , mathematical analysis , bandwidth (computing) , quantum mechanics
In this paper, evolutionary extreme learning machine (E‐ELM) is first introduced for RF power amplifiers (PAs) behavioral modeling. This approach combined differential evolution (DE) and extreme learning machine (ELM) to effectively solve the problem that more neurons of hidden layer are required, and repeated trials are necessary in behavioral modeling PAs by conventional ELM. As revealed in the modeling practices on Class‐AB and Class‐E PAs, fewer hidden layer neurons are used than the condition of conventional ELM. Meanwhile, it is found that ELM's unstable generalization ability in modeling PAs is also significantly improved, thanks to the internal DE method in the E‐ELM.