INTELLIGENT MATCHING TECHNIQUE FOR FLEXIBLE ANTENNAS

Flexible antennas have revolutionized the wireless communication as integral components of modern smart devices. Their unique properties are design flexibility, enhanced performance, and seamless implementation in smart devices. However, when designing antennas, multiple conflicting objectives often need to be considered simultaneously. Incorporating artificial neural networks into optimization strategies has shown promising results in antenna design problems. Neural networks can adapt to different and changeable requirements and constraints. That is why they are valuable tools for customizing antennas to specific operating conditions. The utilization of artificial neural networks for the design of flexible antennas enables researchers to expand the design space, optimize antenna characteristics with greater efficiency, and identify innovative solutions that may not be apparent through traditional design methods. In this study, the authors propose to determine required parameters and characteristics of flexible antennas by using Artificial Intelligence techniques, namely fuzzy logic, neural networks, and genetic algorithms. A matching technique based on neural network for designing flexible antennas has been elaborated. A neural network was developed. To train the neural network, several samples of flexible antenna were manufactured and tested. The developed neural network was simulated. Finally, the obtained flexible antenna was tested.