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

    Authors

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

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    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

    Share

    COinS