NDLI: Dynamical optimal training for behavioral modeling of nonlinear circuit elements based on radial basis function neural network

Content Provider	IEEE Xplore Digital Library
Author	Ming-Jen Kuo Tsung-Chih Lin
Copyright Year	2008
Description	Author affiliation: Grad. Inst. of Electr. & Commun. Eng., Feng-Chia Univ., Taichung (Ming-Jen Kuo)
Abstract	The thorough electromagnetic compatibility (EMC) and signal integrity (SI) modeling of a complete system is a very challenging task due to the extreme complexity of high-speed applications. This paper describes a new methodology for the behavioral modeling based on radial basis function neural network (RBFNN). The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration during the back propagation process can be found. The performance of the dynamic optimal learning rates (DOLR) for RBFNN is discussed by minimizing the mean square error (MSE) between actual and measured outputs for the set of neural network. From the experimental results, we can find that the proposed modeling approach enables to generate accurate behavioral model from measurement carried out on devices operating in their application environments.
Starting Page	670
Ending Page	673
File Size	360083
Page Count	4
File Format	PDF
ISBN	9789810806293
DOI	10.1109/APEMC.2008.4559964
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-19
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Asia Pacific EMC Society
Subject Keyword	dynamic optimal learning rates Electromagnetic compatibility Nonlinear circuits black-box Parametric statistics Degradation Electromagnetic measurements radial basis function Neural networks macromodel Radial basis function networks Electromagnetic modeling Frequency Logic devices
Content Type	Text
Resource Type	Article

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