Evolutionary extreme learning machine for the behavioral modeling of RF power amplifiers

Abstract

AbstractIn 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.