KFU. Card publication. Entropy-based method of reducing the training set dimension at constructing a neuromorphic fault dictionary for analog and mixed-signal ICs

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ENTROPY-BASED METHOD OF REDUCING THE TRAINING SET DIMENSION AT CONSTRUCTING A NEUROMORPHIC FAULT DICTIONARY FOR ANALOG AND MIXED-SIGNAL ICS

Form of presentation

Articles in international journals and collections

Year of publication

2018

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Authors, employees of KFU

Mosin Sergey Gennadevich, author

Bibliographic description in the original language

Mosin S., Entropy-based method of reducing the training set dimension at constructing a neuromorphic fault dictionary for analog and mixed-signal ICs//2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings. - 2018. - Vol., Is.. - P.1-4.

Annotation

The effect of faults on the circuit operating is usually evaluated by simulation, using the appropriate models of catastrophic and parametric faults. Functional and structural complexity, consideration of the tolerances on the components parameters, the infinity of the set of parametric faults, etc., leads to the accumulation of large amounts of data describing the behavior of the fault-free and faulty states of the circuit. Machine learning methods are actively used to construct neuromorphic fault dictionaries (NFD), which provide fault diagnostics of analog and mixed-signal integrated circuits. Many problems of training a neural network with a large amount of data can be solved by reducing the size of training sets and using only significant characteristics in them. The paper proposes a method based on calculating entropy to select the essential characteristics for a training set. An algorithm for reducing the dimension of a training set using entropy is presented.

Keywords

fault diagnostics, analog and mixed-signal integrated circuits, neuromorphic fault dictionary, entropy, training set reduction, machine learning

The name of the journal

2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050683658&doi=10.1109%2fMECO.2018.8406093&partnerID=40&md5=6e51be67b1d1e7b168eaa4e09c4b2b2a

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=185202&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Mosin Sergey Gennadevich	ru_RU
dc.date.accessioned	2018-01-01T00:00:00Z	ru_RU
dc.date.available	2018-01-01T00:00:00Z	ru_RU
dc.date.issued	2018	ru_RU
dc.identifier.citation	Mosin S., Entropy-based method of reducing the training set dimension at constructing a neuromorphic fault dictionary for analog and mixed-signal ICs//2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings. - 2018. - Vol., Is.. - P.1-4.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=185202&p_lang=2	ru_RU
dc.description.abstract	2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings	ru_RU
dc.description.abstract	The effect of faults on the circuit operating is usually evaluated by simulation, using the appropriate models of catastrophic and parametric faults. Functional and structural complexity, consideration of the tolerances on the components parameters, the infinity of the set of parametric faults, etc., leads to the accumulation of large amounts of data describing the behavior of the fault-free and faulty states of the circuit. Machine learning methods are actively used to construct neuromorphic fault dictionaries (NFD), which provide fault diagnostics of analog and mixed-signal integrated circuits. Many problems of training a neural network with a large amount of data can be solved by reducing the size of training sets and using only significant characteristics in them. The paper proposes a method based on calculating entropy to select the essential characteristics for a training set. An algorithm for reducing the dimension of a training set using entropy is presented.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	fault diagnostics	ru_RU
dc.subject	analog and mixed-signal integrated circuits	ru_RU
dc.subject	neuromorphic fault dictionary	ru_RU
dc.subject	entropy	ru_RU
dc.subject	training set reduction	ru_RU
dc.subject	machine learning	ru_RU
dc.title	Entropy-based method of reducing the training set dimension at constructing a neuromorphic fault dictionary for analog and mixed-signal ICs	ru_RU
dc.type	Articles in international journals and collections	ru_RU