An efficient algorithm for electromagnetic scattering by a set of perfect conducting cylindrical objects using the artificial neural network. Issue 4 (13th December 2018)

• Record Type: Journal Article
• Authors: Ines, Jarboua; Ammar, Noemen; Aguili, Taoufik; Baudrand, Henri
• Is Part Of: International journal of RF and microwave computer-aided engineering. Volume 29:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-13
• Subjects: arbitrarily shaped objects -- artificial neural network -- cylindrical coordinates -- electromagnetic coupling -- electromagnetic scattering -- iterative method -- RPROP -- supervised learning; Microwave devices -- Computer-aided design -- Periodicals; Computer-aided engineering -- Periodicals; 621.3813
• DOI: 10.1002/mmce.21593
• Languages: English
• ISSNs: 1096-4290

Abstract

Abstract : In this article an efficient algorithm based on the wave concept iterative process is formulated and applied in order to investigate the electromagnetic scattering by a set of conducting arbitrarily shaped objects placed in the free space. In this approach, the cylindrical coordinates system is used as a modal base in order to develop the modal coefficients of the diffraction operator. This operator models the reaction of the environment expressing the electromagnetic coupling between each two pixels of the discretized surface. A study of electromagnetic coupling between two pixels positioned and oriented somehow in the free space is highlighted so as to determine the coupling operator for different positions and orientations of the emitted and received waves. Twelve coupling operators are developed. For a complex geometric shape structure, the determination of these characteristics involved the determination of interactions between each pixel and the others that constitute the discretized surface. This interaction involves the wave formulation already highlighted for each couple of pixels which implies a higher calculation time. In order to optimize the calculation time, the artificial neural networks are adopted with the feed‐forward architecture. The supervised learning has been chosen with the use of the resilient backpropagation algorithm.