Premium
Modeling the resonant frequency of compact microstrip antenna by the PSO‐based SVM with the hybrid kernel function
Author(s) -
FeiYan Sun,
YuBo Tian,
ZuoLin Ren
Publication year - 2016
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.2171
Subject(s) - particle swarm optimization , support vector machine , kernel (algebra) , generalization , artificial neural network , cauchy distribution , computer science , polynomial kernel , radial basis function kernel , function (biology) , antenna (radio) , microstrip antenna , algorithm , mathematics , artificial intelligence , mathematical optimization , kernel method , telecommunications , mathematical analysis , combinatorics , evolutionary biology , biology
Summary A methodology based on the support vector machine (SVM) combined with a hybrid kernel function (HKF) for accurately modeling the resonant frequencies of the compact microstrip antenna (MSA) is presented and dedicated to reduce the number of samples and simplify the structure when predicting the resonant frequency of the compact MSA by artificial neural network. The parameters of the SVMs and weight coefficients of the HKF are optimized by means of particle swarm optimization algorithm. In addition, two different kernel functions (KFs), namely polynomial KF (a kind of global KF) and Cauchy KF (a kind of local KF), are employed to overcome the disadvantages of traditional KF. The proposed method is validated by the UCI database. The evaluation results show that the HKF can improve the learning ability and generalization ability of the SVM. Furthermore, the resonant frequencies of a planar inverted F‐shaped antenna and an L‐shaped MSA are modeled by the proposed method. Predictive results with high accuracy demonstrate that the particle swarm optimization‐based SVM with the HKF can improve the prediction accuracy for a small dataset. Copyright © 2016 John Wiley & Sons, Ltd.