NDLI: Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Jianfeng Zhai Jianyi Zhou Lei Zhang Wei Hong
Copyright Year	2001
Abstract	In this letter, dynamic fuzzy neural networks (D-FNN) are applied to model power amplifiers (PAs) with memory effects. The D-FNN model implements Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial bias function (RBF) neural networks. The parameters of the model are trained by the online self-organized learning algorithm, in which the neurons can be recruited or deleted dynamically according to their significance to system performance, and the over fitting or over training problems can be avoided. The D-FNN model is validated in our test bench in which a Doherty PA is excited with 10 MHz and 20 MHz worldwide interoperability for microwave access (WiMAX) signals. Experimental results show that the D-FNN model can give an accurate approximation to characterize the wideband PAs with memory effects.
Sponsorship	IEEE Microwave Theory and Techniques Society
Starting Page	528
Ending Page	530
Page Count	3
File Size	300864
File Format	PDF
ISSN	15311309
Volume Number	20
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power amplifiers Fuzzy neural networks Power system modeling Takagi-Sugeno-Kang model Fuzzy systems Neural networks Neurons Recruitment System performance Testing power amplifer (PA) Dynamic fuzzy neural networks (D-FNN) memory effects
Content Type	Text
Resource Type	Article
Subject	Condensed Matter Physics Electrical and Electronic Engineering

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