Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks
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...
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| Veröffentlicht in: | IEEE microwave and wireless components letters 2010-09, Vol.20 (9), p.528-530 |
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| creator | Zhai, Jianfeng Zhou, Jianyi Zhang, Lei Hong, Wei |
| description | 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. |
| doi_str_mv | 10.1109/LMWC.2010.2052594 |
| format | Article |
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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.</description><identifier>ISSN: 1531-1309</identifier><identifier>ISSN: 2771-957X</identifier><identifier>EISSN: 1558-1764</identifier><identifier>EISSN: 2771-9588</identifier><identifier>DOI: 10.1109/LMWC.2010.2052594</identifier><identifier>CODEN: IMWCBJ</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Amplifiers ; Applied sciences ; Circuit properties ; Dynamic fuzzy neural networks (D-FNN) ; Dynamics ; Electric, optical and optoelectronic circuits ; Electronic circuits ; Electronic equipment and fabrication. Passive components, printed wiring boards, connectics ; Electronics ; Exact sciences and technology ; Fuzzy ; Fuzzy logic ; Fuzzy neural networks ; Fuzzy set theory ; Fuzzy systems ; Mathematical models ; memory effects ; Microwaves ; Neural networks ; Neurons ; power amplifer (PA) ; Power amplifiers ; Power system modeling ; Recruitment ; System performance ; Takagi-Sugeno-Kang model ; Testing</subject><ispartof>IEEE microwave and wireless components letters, 2010-09, Vol.20 (9), p.528-530</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-4dd9ee60f0ffd04edda06b3fe3c71c5509b56312b2165927486dad827b9cc3983</citedby><cites>FETCH-LOGICAL-c355t-4dd9ee60f0ffd04edda06b3fe3c71c5509b56312b2165927486dad827b9cc3983</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5504068$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5504068$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23204631$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhai, Jianfeng</creatorcontrib><creatorcontrib>Zhou, Jianyi</creatorcontrib><creatorcontrib>Zhang, Lei</creatorcontrib><creatorcontrib>Hong, Wei</creatorcontrib><title>Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks</title><title>IEEE microwave and wireless components letters</title><addtitle>LMWC</addtitle><description>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.</description><subject>Algorithms</subject><subject>Amplifiers</subject><subject>Applied sciences</subject><subject>Circuit properties</subject><subject>Dynamic fuzzy neural networks (D-FNN)</subject><subject>Dynamics</subject><subject>Electric, optical and optoelectronic circuits</subject><subject>Electronic circuits</subject><subject>Electronic equipment and fabrication. Passive components, printed wiring boards, connectics</subject><subject>Electronics</subject><subject>Exact sciences and technology</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy set theory</subject><subject>Fuzzy systems</subject><subject>Mathematical models</subject><subject>memory effects</subject><subject>Microwaves</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>power amplifer (PA)</subject><subject>Power amplifiers</subject><subject>Power system modeling</subject><subject>Recruitment</subject><subject>System performance</subject><subject>Takagi-Sugeno-Kang model</subject><subject>Testing</subject><issn>1531-1309</issn><issn>2771-957X</issn><issn>1558-1764</issn><issn>2771-9588</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMtu2zAQRYUiAZrXBxTZCAiKruRw-JK4TJ26TWAnXbTIUqCpYUxHFh3SquF8fSnYyCKrmcGcO5h7s-wLkBEAUdfT2dN4REkaKRFUKP4pOwEhqgJKyY-GnkEBjKjP2WmMS0KAVxxOsvvvuND_nA-6zWe-wdZ1z7m3-W-_xZDfrNatsw5DzJ_cZpHf7jq9ciaf9G9vu_wB-0H2gJutDy_xPDu2uo14cahn2d_Jjz_jX8X08efd-GZaGCbEpuBNoxAlscTahnBsGk3knFlkpgQjBFFzIRnQOQUpFC15JRvdVLScK2OYqthZ9m1_dx38a49xU69cNNi2ukPfx7qsSlpCyWgirz6QS9-HLj1XA6GqkiCBJwr2lAk-xoC2Xge30mGXoHoItx7CrYdw60O4SfP1cFlHo1sbdGdcfBdSRglPJhJ3ueccIr6vk0lOZMX-Ay7tgWg</recordid><startdate>20100901</startdate><enddate>20100901</enddate><creator>Zhai, Jianfeng</creator><creator>Zhou, Jianyi</creator><creator>Zhang, Lei</creator><creator>Hong, Wei</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Passive components, printed wiring boards, connectics</topic><topic>Electronics</topic><topic>Exact sciences and technology</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy set theory</topic><topic>Fuzzy systems</topic><topic>Mathematical models</topic><topic>memory effects</topic><topic>Microwaves</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>power amplifer (PA)</topic><topic>Power amplifiers</topic><topic>Power system modeling</topic><topic>Recruitment</topic><topic>System performance</topic><topic>Takagi-Sugeno-Kang model</topic><topic>Testing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhai, Jianfeng</creatorcontrib><creatorcontrib>Zhou, Jianyi</creatorcontrib><creatorcontrib>Zhang, Lei</creatorcontrib><creatorcontrib>Hong, Wei</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE microwave and wireless components letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhai, Jianfeng</au><au>Zhou, Jianyi</au><au>Zhang, Lei</au><au>Hong, Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks</atitle><jtitle>IEEE microwave and wireless components letters</jtitle><stitle>LMWC</stitle><date>2010-09-01</date><risdate>2010</risdate><volume>20</volume><issue>9</issue><spage>528</spage><epage>530</epage><pages>528-530</pages><issn>1531-1309</issn><issn>2771-957X</issn><eissn>1558-1764</eissn><eissn>2771-9588</eissn><coden>IMWCBJ</coden><abstract>In this letter, dynamic fuzzy neural networks (D-FNN) are applied to model power amplifiers (PAs) with memory effects. 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| subjects | Algorithms Amplifiers Applied sciences Circuit properties Dynamic fuzzy neural networks (D-FNN) Dynamics Electric, optical and optoelectronic circuits Electronic circuits Electronic equipment and fabrication. Passive components, printed wiring boards, connectics Electronics Exact sciences and technology Fuzzy Fuzzy logic Fuzzy neural networks Fuzzy set theory Fuzzy systems Mathematical models memory effects Microwaves Neural networks Neurons power amplifer (PA) Power amplifiers Power system modeling Recruitment System performance Takagi-Sugeno-Kang model Testing |
| title | Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks |
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