Radial Basis Function Neural Network Optimal Modeling for Phase-Only Array Pattern Nulling

Autor:	Wang Zhaoping, Xun Qi, Huiling Zhao, Mingxuan Zheng, Zhonghui Zhao
Rok vydání:	2021
Předmět:	Pattern synthesis Computer science Generalization Radial basis function neural Differential evolution Computer Science::Neural and Evolutionary Computation Process (computing) Phase (waves) Electrical and Electronic Engineering Algorithm Phase adjustment Optimal modeling
Zdroj:	IEEE Transactions on Antennas and Propagation. 69:7971-7975
ISSN:	1558-2221 0018-926X
DOI:	10.1109/tap.2021.3083787
Popis:	Phase-only nulling with low sidelobe level is a problem of interest in array synthesis which is a tedious problem without an analytical solution. In this communication, a novel framework for the phase-only nulling based on radial basis function neural network (RBFNN) is proposed to predict the phase adjustment for the array pattern nulling with sidelobe control. In the process of network training, the parameters of the RBFNN are optimized simultaneously based on the self-adaptive differential evolution (SADE) algorithm, which aims to improve the approximation ability and reduce the complexity of the network. Simulation results of the optimized RBFNN models show compact network structure and form the array pattern under desired performance with good generalization capability.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::90211199b9d8e803c7998362cc114ef7 https://doi.org/10.1109/tap.2021.3083787 Zobrazit plný text záznamu