Support Vector Driven Genetic Algorithm for the Design of Circular Polarized Microstrip Antenna |
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Authors: | Narendra Chauhan Ankush Mittal and M V Kartikeyan |
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Institution: | (1) Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, 247 667, India |
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Abstract: | In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm
such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear
feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized
is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging,
etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate
one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This
approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model
are compared with other meta-models generated with neural network and response surface methodology. |
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Keywords: | Support vector regression Genetic algorithms Microstrip antennas |
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