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Journal of Artificial Intelligence
  Year: 2018 | Volume: 11 | Issue: 2 | Page No.: 79-84
DOI: 10.3923/jai.2018.79.84
 
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Investigation of an Efficient RF-MEMS Switch for Reconfigurable Antenna Using Hybrid Algorithm with Artificial Neural Network
Qazi Fasihuddin Zahuruddin and Mulpuri Sri Rukmini

Abstract:
Background and Objective: As MEMS (Micro Electro Mechanical System) technology continuously growing, MEMS for reconfigurable antenna design and optimization is becoming an interesting and important research issue. Many RF-MEMS reconfigurable antenna design focused on changing operating frequency while sustaining their radiation characteristics. However, to enhance the performance of the antenna it was proposed to change the radiation characteristics and keeping the operating frequency constant. Thus for achieving this, the main objective of this investigation study was to design efficient RF-MEMS switch for reconfigurable antenna using hybrid optimization algorithm with Artificial Neural Network. Materials and Methods: In this proposed research method, for optimization of RF-MEMS switch, parameters like beam length, beam width, switch thickness, torsion arm thickness, holes and gaps are considered and to get optimized parameters, gravitational search optimization algorithm is intended with artificial neural network which has been implemented on the working platform of MATLAB. Results: The simulated result indicates that the hybrid algorithm enhanced the global search ability and gives the reasonably good accuracy and reduction in mean square error and Bit error rate. Conclusion: Finally after comparing our proposed technique with existing techniques, concluding that we are getting efficient RF-MEMS switch for reconfigurable antenna and performance of the system is increasing.
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How to cite this article:

Qazi Fasihuddin Zahuruddin and Mulpuri Sri Rukmini, 2018. Investigation of an Efficient RF-MEMS Switch for Reconfigurable Antenna Using Hybrid Algorithm with Artificial Neural Network. Journal of Artificial Intelligence, 11: 79-84.

DOI: 10.3923/jai.2018.79.84

URL: https://scialert.net/abstract/?doi=jai.2018.79.84

 
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