Title

On The Application Of A Neural-Network In The Design Of Cascaded Gratings

Authors

Authors

C. G. Christodoulou; J. Huang; M. Georgiopoulos;J. J. Liou

Comments

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Abbreviated Journal Title

Microw. Opt. Technol. Lett.

Keywords

GRATINGS; SCATTERING; NEURAL NETWORKS; Engineering, Electrical & Electronic; Optics

Abstract

This article presents a study of the ARTMAP neural network in designing cascaded gratings. A neural network can be trained to keep changing the dimensions of the metallic strips, their distance of separation, their width, the number of layers required in a multilayer structure, and the angle of wave incidence until the frequency response of the structure matches the desired one. In the past, the back-propagation (back-prop) learning algorithm was used in conjunction with an inversion algorithm for the design of frequency-selective surfaces. Unfortunately, both the back-prop algorithm and the inversion procedure are slow to converge. In this work the Fuzzy ARTMAP neural network is utilized. The fuzzy ARTMAP is faster to train than the back-prop, and it does not require an inversion algorithm. Several results (frequency responses) from cascaded gratings for various angles of wave incidence, layer separation, width strips, and interstrip separation are presented and discussed. (C) 1995 John Wiley & Sons, Inc.

Journal Title

Microwave and Optical Technology Letters

Volume

8

Issue/Number

4

Publication Date

1-1-1995

Document Type

Article

Language

English

First Page

171

Last Page

175

WOS Identifier

WOS:A1995QH66000001

ISSN

0895-2477

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