Abstract
A neural-network approach is presented for the electromagnetic imaging of conducting cylinders of elliptical cross section. Simulation results are presented which demonstrate that a properly trained neural network is capable of accurately identifying the shape and location of unknown elliptic cylinders in inaccessible domains. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 28: 303–306, 2001.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
