University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Smart modeling of microwave devices

Kabir, H, Zhang, Q-J, Yu, M, Zhang, L, Aaen, PH and Wood, J (2010) Smart modeling of microwave devices IEEE Microwave Magazine, 11 (3). pp. 105-118.

[img]
Preview
PDF
MicrowaveMagazine_Kabir_V20_Revision.pdf
Available under License : See the attached licence file.

Download (924kB)
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf

Download (33kB)

Abstract

An overview of neural network-based modeling techniques and their applications in microwave modeling and design is presented. The neural network represent RF/microwave components with the help of training data that are pairs of model input-output (IO) data generated from detailed microwave simulation or measurement. Neural networks have significant advantages over other techniques for multidimensional function approximation as they permit a compact representation of a multidimensional function, requiring minimal storage of coefficients and being very efficient to evaluate. The neural network can produce a parametric model by exploiting existing microwave knowledge in the form of empirical/analytical/equivalent model during neural network development. Neural network maps an existing model to match a new device with a technique called Neuro-Space Mapping (Neuro-SM).

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Advanced Technology Institute
Authors :
AuthorsEmailORCID
Kabir, HUNSPECIFIEDUNSPECIFIED
Zhang, Q-JUNSPECIFIEDUNSPECIFIED
Yu, MUNSPECIFIEDUNSPECIFIED
Zhang, LUNSPECIFIEDUNSPECIFIED
Aaen, PHUNSPECIFIEDUNSPECIFIED
Wood, JUNSPECIFIEDUNSPECIFIED
Date : May 2010
Identification Number : 10.1109/MMM.2010.936079
Additional Information : © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 26 Sep 2013 14:05
Last Modified : 09 Jun 2014 13:36
URI: http://epubs.surrey.ac.uk/id/eprint/803449

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800