Support Vector Regression for the Modeling and Synthesis of Near-Field Focused Antenna Arrays

Abstract

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented.

Application of Machine Learning in Electromagnetics: Mini-Review

“…However, studies have shown that the Radial Basis Functions (RBF) of ANN are best suitable for evaluating the resonant frequency of rectangular microstrip patch antennae [55]. Radiation field estimation utilizing the nearfield focusing technique offered by an earlier report [56] showed faster performance of the SVM model with a smaller training dataset. Investigators [57] developed an inverse NN (INN) model that could determine the VSWR, gain, radiation pattern, and radiation efficiency of different planes with greater accuracy using a small dataset for training, with only 36 samples.…”

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

“…Data-driven surrogate modeling has proved its usage in the design procedure of highfrequency devices as a low-cost surrogate of the various electrical and field responses of high-frequency stages such as scattering parameters [S], 14,15 reflection phase characteristics in reflect-arrays, 16 characteristic impedance, 17 and prediction resonant frequency of antenna designs. [18][19][20] In each of the mentioned works, different types of Artificial Intelligence (AI) regression methods such as polynomial, 21,22 kriging, [23][24][25] Support Vector Regression (SVR), [26][27][28][29] Artificial Neural Networks (ANNs), [30][31][32][33][34] and Deep Learning (DL) [35][36][37][38][39] had been used to create an accurate, stable mapping between the given input space of the problem and the targeted characteristic as the output of the model.…”

A Novel Non-Isotonic Statistical Bivariate Regression Method—Application to Stratigraphic Data Modeling and Interpolation

“…One solution is to infer a functional relationship between variables using regression analysis as illustrated, to cite a few, in the paper [2] on evolutionary algorithms, in the contribution [3] on autonomous agents, and in the contributions [4][5][6] which cover several practical aspects of regression analysis. Regression is a computation application of paramount importance as testified by the research paper [7] that illustrates an application to drowsiness estimation using electroencephalographic data, by the book [8] on statistical methods for engineers and scientists, by [9] that explores an improved power law for nonlinear least-squares fitting, in the papers [10][11][12] that exploit regression analysis in forecasting and prediction, by the research paper [13] that compares a number of linear and non-linear regression methods, in the paper [14] that uses support vector regression for the modeling and synthesis of antenna arrays, and by the contribution [15] that applies kernel Ridge regression to short-term wind speed forecasting.…”