Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks. (27th March 2014)

• Record Type: Journal Article
• Authors: Khan, Taimoor; De, Asok
• Other Names: Hartnagel H. L. Academic Editor.; Mao L.-F. Academic Editor.; Wang Y.-H. Academic Editor.
• Is Part Of: ISRN electronics. Volume 2014(2014)
• Publication Date: 2014-03-27
• Subjects: Electronics -- Periodicals; Electronics; Electronic journals; Periodicals; 621.381
• DOI: 10.1155/2014/296105
• Languages: English
• ISSNs: 2090-8679

Abstract

Abstract : Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger's substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.