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A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA
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| Zeitschriftentitel: | Journal of Artificial Intelligence and Soft Computing Research |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Journal of Artificial Intelligence and Soft Computing Research, 9, 2019, 1, S. 5-19 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina |
|---|---|
| author |
Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina |
| spellingShingle |
Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina Journal of Artificial Intelligence and Soft Computing Research A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| author_sort |
aizenberg, igor |
| spelling |
Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0021 <jats:title>Abstract</jats:title> <jats:p>A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.</jats:p> A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA Journal of Artificial Intelligence and Soft Computing Research |
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| title |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_unstemmed |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_full |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_fullStr |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_full_unstemmed |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_short |
A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_sort |
a mlmvn with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using fra |
| topic |
Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| url |
http://dx.doi.org/10.2478/jaiscr-2018-0021 |
| publishDate |
2019 |
| physical |
5-19 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.</jats:p> |
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| author | Aizenberg, Igor, Luchetta, Antonio, Manetti, Stefano, Piccirilli, Maria Cristina |
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| description | <jats:title>Abstract</jats:title> <jats:p>A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.</jats:p> |
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| spelling | Aizenberg, Igor Luchetta, Antonio Manetti, Stefano Piccirilli, Maria Cristina 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0021 <jats:title>Abstract</jats:title> <jats:p>A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.</jats:p> A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA Journal of Artificial Intelligence and Soft Computing Research |
| spellingShingle | Aizenberg, Igor, Luchetta, Antonio, Manetti, Stefano, Piccirilli, Maria Cristina, Journal of Artificial Intelligence and Soft Computing Research, A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| title | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_full | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_fullStr | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_full_unstemmed | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_short | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| title_sort | a mlmvn with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using fra |
| title_unstemmed | A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA |
| topic | Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| url | http://dx.doi.org/10.2478/jaiscr-2018-0021 |