Diagonal Recurrent Neural Network-Based Hysteresis Modeling
The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the firs...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2022-12, Vol.33 (12), p.7502-7512 |
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| creator | Chen, Guangzeng Chen, Guangke Lou, Yunjiang |
| description | The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the first time, the hysteresis nature and conditions of the classical dRNN with the tanh activation function are mathematically discovered and investigated, instead of using the common black-box approach and its variants. It is shown that the dRNN neuron is a versatile rate-dependent hysteresis system under specific conditions. The dRNN composed of those neurons can be used for modeling the rate-dependent hysteresis and it can approximate the Preisach model with arbitrary precision with specific parameters for rate-independent hysteresis modeling. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model. |
| doi_str_mv | 10.1109/TNNLS.2021.3085321 |
| format | Article |
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In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the first time, the hysteresis nature and conditions of the classical dRNN with the tanh activation function are mathematically discovered and investigated, instead of using the common black-box approach and its variants. It is shown that the dRNN neuron is a versatile rate-dependent hysteresis system under specific conditions. The dRNN composed of those neurons can be used for modeling the rate-dependent hysteresis and it can approximate the Preisach model with arbitrary precision with specific parameters for rate-independent hysteresis modeling. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2021.3085321</identifier><identifier>PMID: 34143742</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Artificial neural networks ; Computational modeling ; Hysteresis ; Hysteresis modeling ; Hysteresis models ; Mathematical models ; Modelling ; Neural networks ; Neural Networks, Computer ; Neurons ; Preisach model ; Preisach's theory ; recurrent neural network (RNN) ; Recurrent neural networks ; Switches</subject><ispartof>IEEE transaction on neural networks and learning systems, 2022-12, Vol.33 (12), p.7502-7512</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-b394a3892f11a48a5a45f2142b85b09db342f97659a0b6a569bc3b148d12f0bb3</citedby><cites>FETCH-LOGICAL-c351t-b394a3892f11a48a5a45f2142b85b09db342f97659a0b6a569bc3b148d12f0bb3</cites><orcidid>0000-0002-4767-5908 ; 0000-0001-6873-747X ; 0000-0001-8203-7795</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9460309$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9460309$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34143742$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Guangzeng</creatorcontrib><creatorcontrib>Chen, Guangke</creatorcontrib><creatorcontrib>Lou, Yunjiang</creatorcontrib><title>Diagonal Recurrent Neural Network-Based Hysteresis Modeling</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. 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Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.</description><subject>Artificial neural networks</subject><subject>Computational modeling</subject><subject>Hysteresis</subject><subject>Hysteresis modeling</subject><subject>Hysteresis models</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Neurons</subject><subject>Preisach model</subject><subject>Preisach's theory</subject><subject>recurrent neural network (RNN)</subject><subject>Recurrent neural networks</subject><subject>Switches</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkE1Lw0AQhhdRbKn9AwoS8OIldT-TLJ60flSoEbSCt2U3mZTUNKm7CdJ_79bWHtzLLDPPvDAPQqcEjwjB8mqWptO3EcWUjBhOBKPkAPUpiWhIWZIc7v_xRw8NnVtg_yIsIi6PUY9xwlnMaR9d35V63tS6Cl4h66yFug1S6KxvpNB-N_YzvNUO8mCydi1YcKULnpscqrKen6CjQlcOhrs6QO8P97PxJJy-PD6Nb6ZhxgRpQ8Mk1yyRtCBE80QLzUVBCacmEQbL3DBOCxlHQmpsIi0iaTJmCE9yQgtsDBugy23uyjZfHbhWLUuXQVXpGprOKSo448KnxB69-Icums768zzl740xkzH1FN1SmW2cs1ColS2X2q4VwWpjV_3aVRu7amfXL53vojuzhHy_8ufSA2dboASA_VjyCDMs2Q8MCXvX</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Chen, Guangzeng</creator><creator>Chen, Guangke</creator><creator>Lou, Yunjiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Artificial neural networks Computational modeling Hysteresis Hysteresis modeling Hysteresis models Mathematical models Modelling Neural networks Neural Networks, Computer Neurons Preisach model Preisach's theory recurrent neural network (RNN) Recurrent neural networks Switches |
| title | Diagonal Recurrent Neural Network-Based Hysteresis Modeling |
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