Behavioral Modeling of Memristor-Based Rectifier Bridge
In electrical engineering, radio engineering, robotics, computing, control systems, etc., a lot of nonlinear devices are synthesized on the basis of a nanoelement named memristor that possesses a number of useful properties, such as passivity, nonlinearity, high variability of parameters, nonvolatil...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/7/2908 |
| _version_ | 1827696835486023680 |
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| author | Elena Solovyeva Steffen Schulze Hanna Harchuk |
| author_facet | Elena Solovyeva Steffen Schulze Hanna Harchuk |
| author_sort | Elena Solovyeva |
| collection | DOAJ |
| description | In electrical engineering, radio engineering, robotics, computing, control systems, etc., a lot of nonlinear devices are synthesized on the basis of a nanoelement named memristor that possesses a number of useful properties, such as passivity, nonlinearity, high variability of parameters, nonvolatility, compactness. The efficiency of this electric element has led to the emergence of many memristor technologies based on different physical principles and, as a result, to the occurrence of different mathematical models describing these principles. A general approach to the modeling of memristive devices is represented. The essence is to construct a behavioral model that approximates nonlinear mapping of the input signal set into the output signal set. The polynomials of split signals, which are adaptive to the class of input signals, are used. This adaptation leads to the model’s simplification important in practice. Multi-dimensional polynomials of split signals are built for the rectifier bridge at harmonic input signals. The modeling error is estimated in the mean-square norm. It is shown that the accuracy of the modeling is increased in the case of using the piecewise polynomial with split signals. |
| first_indexed | 2024-03-10T12:55:54Z |
| format | Article |
| id | doaj.art-3781bb5d68fb454a9e216f6aa5e0a73a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:55:54Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3781bb5d68fb454a9e216f6aa5e0a73a2023-11-21T11:52:58ZengMDPI AGApplied Sciences2076-34172021-03-01117290810.3390/app11072908Behavioral Modeling of Memristor-Based Rectifier BridgeElena Solovyeva0Steffen Schulze1Hanna Harchuk2Department of Electrical Engineering Theory, Saint Petersburg Electrotechnical University “LETI”, 197376 St. Petersburg, RussiaWuerth Elektronik eiSos GmbH, 74638 Waldenburg, GermanyDepartment of Electrical Engineering Theory, Saint Petersburg Electrotechnical University “LETI”, 197376 St. Petersburg, RussiaIn electrical engineering, radio engineering, robotics, computing, control systems, etc., a lot of nonlinear devices are synthesized on the basis of a nanoelement named memristor that possesses a number of useful properties, such as passivity, nonlinearity, high variability of parameters, nonvolatility, compactness. The efficiency of this electric element has led to the emergence of many memristor technologies based on different physical principles and, as a result, to the occurrence of different mathematical models describing these principles. A general approach to the modeling of memristive devices is represented. The essence is to construct a behavioral model that approximates nonlinear mapping of the input signal set into the output signal set. The polynomials of split signals, which are adaptive to the class of input signals, are used. This adaptation leads to the model’s simplification important in practice. Multi-dimensional polynomials of split signals are built for the rectifier bridge at harmonic input signals. The modeling error is estimated in the mean-square norm. It is shown that the accuracy of the modeling is increased in the case of using the piecewise polynomial with split signals.https://www.mdpi.com/2076-3417/11/7/2908behavioral modelingnonlinear modelpolynomialnonlinear dynamic systemmemristormemristive device |
| spellingShingle | Elena Solovyeva Steffen Schulze Hanna Harchuk Behavioral Modeling of Memristor-Based Rectifier Bridge Applied Sciences behavioral modeling nonlinear model polynomial nonlinear dynamic system memristor memristive device |
| title | Behavioral Modeling of Memristor-Based Rectifier Bridge |
| title_full | Behavioral Modeling of Memristor-Based Rectifier Bridge |
| title_fullStr | Behavioral Modeling of Memristor-Based Rectifier Bridge |
| title_full_unstemmed | Behavioral Modeling of Memristor-Based Rectifier Bridge |
| title_short | Behavioral Modeling of Memristor-Based Rectifier Bridge |
| title_sort | behavioral modeling of memristor based rectifier bridge |
| topic | behavioral modeling nonlinear model polynomial nonlinear dynamic system memristor memristive device |
| url | https://www.mdpi.com/2076-3417/11/7/2908 |
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