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Gradient based reverse ANN modeling approach for RF/microwave computer aided design

Mareddy, L, Almalkawi, M, Devabhaktuni, V, Vemuru, S, Zhang, L and Aaen, PH (2012) Gradient based reverse ANN modeling approach for RF/microwave computer aided design European Microwave Week 2012: "Space for Microwaves", EuMW 2012, Conference Proceedings - 7th European Microwave Integrated Circuits Conference, EuMIC 2012. pp. 246-249.

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Abstract

In this work, a new gradient based reverse modeling approach employing Artificial Neural Networks (ANNs) for systematic RF/microwave modeling is introduced. This approach is particularly suited to modeling scenarios, where standard ANN multi-layer perceptron (MLP) fails to deliver a satisfactory model. The proposed approach detects the simplest input-output relationship inherent to the modeling problem, which we term as the reverse model as compared to the original model (i.e., the modeling problem using standard ANN model). This reverse model is short-listed from a pool of candidate models obtained by systematically reversing the input-output variables of the original modeling problem, while retaining the ANN's structural simplicity. The proposed reverse and the not-so-Accurate original models complement each other to yield accurate models. The advantages of this approach are demonstrated via modeling transmission lines and spiral inductors. © 2012 European Microwave Assoc.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Advanced Technology Institute
Authors :
AuthorsEmailORCID
Mareddy, LUNSPECIFIEDUNSPECIFIED
Almalkawi, MUNSPECIFIEDUNSPECIFIED
Devabhaktuni, VUNSPECIFIEDUNSPECIFIED
Vemuru, SUNSPECIFIEDUNSPECIFIED
Zhang, LUNSPECIFIEDUNSPECIFIED
Aaen, PHUNSPECIFIEDUNSPECIFIED
Date : 2012
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2012 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 : 04 Oct 2013 15:56
Last Modified : 09 Jun 2014 13:33
URI: http://epubs.surrey.ac.uk/id/eprint/803538

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