Modelling and Optimization of Metamaterial Antennas using Kernel Machine Approach
Journal Title: JOURNAL OF ADVANCED RESEARCH IN MECHANICAL ENGINEERING AND TECHNOLOGY - Year 2018, Vol 5, Issue 3
Abstract
This research work is concerned with the application of Gaussian process regression kernel machine learning approach in modelling a metamaterial antenna. MATLAB Graphical user interface (GUI) was used to randomly select two frequency points within the range 2.5GHz ≤ f ≤ 3GHz and ten points in the range 3GHz ≤ f ≤3.6GHz respectively. The training and test inputs generated for the model were standardized along with the frequency of operation. The rand function in MATLAB was used to generate ten sets of values between 0 and 1. The lowest negative log likelihood value gotten was -1.9762. This value is produced by the gp model with an initial hyper parameter value set at 0.1386. The model likelihood value is -13.8155. The model is capable of predicting optimal antenna responses for any new set of input data associated with the modelled metamaterial antennas.
Authors and Affiliations
Chiagunye Tochukwu,
Keywords
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