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Macromodeling Nonlinear Circuits Using Proper Orthogonal Decomposition and Artificial Neural Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Macromodeling Nonlinear Circuits Using Proper Orthogonal Decomposition and Artificial Neural Networks./
作者:	Kanaan, Marwan.
面頁冊數:	1 online resource (129 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Receivers & amplifiers. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29043528click for full text (PQDT)
ISBN:	9798209935278

Macromodeling Nonlinear Circuits Using Proper Orthogonal Decomposition and Artificial Neural Networks.
Kanaan, Marwan.

Macromodeling Nonlinear Circuits Using Proper Orthogonal Decomposition and Artificial Neural Networks. - 1 online resource (129 pages)

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--McGill University (Canada), 2021.

Includes bibliographical references

Reduced-order macromodels are a useful tool that designers can use to speed up circuit simulations. When carefully constructed, these macromodels can guarantee a high degree of accuracy and replace the original subcircuits under certain conditions, which the designer can also choose. Generating reduced-order macromodels of linear subcircuits has been thoroughly studied in the literature and several algorithms have been proposed which can successfully achieve that. On the other hand, generating nonlinear reduced-order macromodels proved to be a much more difficult task.This thesis presents a general and systematic macromodeling algorithm that can be used to reduce the size of a wide range of nonlinear electronic circuits to speed up simulation. The method is composed of two separate algorithms that are used sequentially. In the first step, the size of equations of the electronic circuit is reduced using Proper Orthogonal Decomposition. The system is projected onto a predefined reduced subspace resulting in massive reduction of circuit size. This first step can only be applied to the linear part of the circuit, and because of the nonlinear nature of electronic circuits, the speedup gained from this reduction remains limited. To alleviate that, the second step targets the nonlinear part. Feedforward neural networks, known for their effectiveness as a curve fitting tool, are used to replace the functions describing the nonlinear part of the circuit.By applying these two steps one after the other, the algorithm generates nonlinear reduced-order macromodels capable of replacing complete electronic circuits. The macromodels can be directly added to a larger system and result, on average, in four to five times speedup in simulation time. These macromodels are valid over a specific range of conditions, such as input power, frequency, or loading conditions, which is chosen by the designer at construction.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798209935278Subjects--Topical Terms:

3559205
Receivers & amplifiers.
Index Terms--Genre/Form:

542853
Electronic books.

Macromodeling Nonlinear Circuits Using Proper Orthogonal Decomposition and Artificial Neural Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
500 $a Advisor: Khazaka, Roni.
502 $a Thesis (Ph.D.)--McGill University (Canada), 2021.
504 $a Includes bibliographical references
520 $a Reduced-order macromodels are a useful tool that designers can use to speed up circuit simulations. When carefully constructed, these macromodels can guarantee a high degree of accuracy and replace the original subcircuits under certain conditions, which the designer can also choose. Generating reduced-order macromodels of linear subcircuits has been thoroughly studied in the literature and several algorithms have been proposed which can successfully achieve that. On the other hand, generating nonlinear reduced-order macromodels proved to be a much more difficult task.This thesis presents a general and systematic macromodeling algorithm that can be used to reduce the size of a wide range of nonlinear electronic circuits to speed up simulation. The method is composed of two separate algorithms that are used sequentially. In the first step, the size of equations of the electronic circuit is reduced using Proper Orthogonal Decomposition. The system is projected onto a predefined reduced subspace resulting in massive reduction of circuit size. This first step can only be applied to the linear part of the circuit, and because of the nonlinear nature of electronic circuits, the speedup gained from this reduction remains limited. To alleviate that, the second step targets the nonlinear part. Feedforward neural networks, known for their effectiveness as a curve fitting tool, are used to replace the functions describing the nonlinear part of the circuit.By applying these two steps one after the other, the algorithm generates nonlinear reduced-order macromodels capable of replacing complete electronic circuits. The macromodels can be directly added to a larger system and result, on average, in four to five times speedup in simulation time. These macromodels are valid over a specific range of conditions, such as input power, frequency, or loading conditions, which is chosen by the designer at construction.
520 $a Les macromodeles d'ordre reduit sont un outil utile que les concepteurs peuvent utiliser pour accelerer les simulations de circuits. Une fois elabores avec soin, ces macromodeles peuvent garantir un haut degre de precision et remplacer les sous-circuits originaux sous certaines conditions, que le concepteur peut egalement choisir. La generation de macromodeles d'ordre reduit de sous-circuits lineaires a ete pleinement etudiee dans la litterature, et plusieurs algorithmes ont ete proposes qui peuvent y parvenir avec succes. Cependant, la generation de macromodeles d'ordre reduit non-lineaires s'est averee etre une tache beaucoup plus difficile.Cette these presente un algorithme de macromodelisation general et systematique qui peut etre utilise pour reduire la taille d'une large gamme de circuits electroniques non-lineaires et en accelerer la simulation. La methode est composee de deux algorithmes distincts qui sont executes sequentiellement. Dans la premiere etape, la taille des equations du circuit electronique est reduite a l'aide d'une decomposition orthogonale aux valeurs propres. Le systeme est projete sur un sous-espace reduit predefini, ce qui entraine une reduction massive de la taille du circuit. Cette premiere etape ne peut s'appliquer qu'a la partie lineaire du circuit, et du fait du caractere non-lineaire des circuits electroniques, l'acceleration obtenue grace a cette reduction reste limitee. Pour remedier a cela, la deuxieme etape cible la partie non-lineaire. Les perceptrons multicouches, connus pour leur efficacite en tant qu'outil de regression, sont utilises pour approximer la partie non lineaire du circuit.En appliquant ces deux etapes l'une apres l'autre, l'algorithme genere des macromodeles non-lineaires d'ordre reduit capables de remplacer des circuits electroniques complets. Les macromodeles peuvent egalement etre directement ajoutes a un systeme plus grand, et peuvent permettre de reduire le temps de simulation par un facteur quatre ou cinq. Ces macromodeles sont valides sur une plage specifique de conditions, telles que la puissance d'entree, la frequence ou les conditions de charge, qui sont choisies par le concepteur lors de la construction.
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538 $a Mode of access: World Wide Web
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