КФУ. Карточка публикации. Detection of quasi-periodic processes in repeated measurements: new approach for the fitting and clusterization of different data

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DETECTION OF QUASI-PERIODIC PROCESSES IN REPEATED MEASUREMENTS: NEW APPROACH FOR THE FITTING AND CLUSTERIZATION OF DIFFERENT DATA

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2014

Авторы, сотрудники КФУ

Нигматуллин Равиль Рашидович, автор

Рахматуллин Рафаиль Мансурович, автор

Библиографическое описание на языке оригинала

R. Nigmatullin, R. Rakhmatullin Detection of quasi-periodic processes in repeated measurements: New approach for the fitting and clusterization of different data //Communications in Nonlinear Science and Numerical Simulation, Volume 19, Issue 12, December 2014, Pages 4080–4093

Аннотация

Many experimentalists were accustomed to think that any independent measurement forms a non-correlated measurement that depends weakly from others. We are trying to reconsider this conventional point of view and prove that similar measurements form a strongly-correlated sequence of random functions with memory. In other words, successive measurements ?remember? each other at least their nearest neighbors. This observation and justification on real data help to fit the wide set of data based on the Prony?s function. The Prony?s decomposition follows from the quasi-periodic (QP) properties of the measured functions and includes the Fourier transform as a partial case. New type of decomposition helps to obtain a specific amplitude?frequency response (AFR) of the measured (random) functions analyzed and each random function contains less number of the fitting parameters in comparison with its number of initial data points. Actually, the calculated AFR can be considered as the generalized

Ключевые слова

The repeated EPR measurements,The quasi-periodic (QP) process, The generalized Prony?s spectrum (GPS)

Название журнала

Communications in Nonlinear Science and Numerical Simulation

URL

http://www.sciencedirect.com/science/article/pii/S1007570414001750

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https://repository.kpfu.ru/?p_id=85554

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Нигматуллин Равиль Рашидович	ru_RU
dc.contributor.author	Рахматуллин Рафаиль Мансурович	ru_RU
dc.date.accessioned	2014-01-01T00:00:00Z	ru_RU
dc.date.available	2014-01-01T00:00:00Z	ru_RU
dc.date.issued	2014	ru_RU
dc.identifier.citation	R. Nigmatullin, R. Rakhmatullin Detection of quasi-periodic processes in repeated measurements: New approach for the fitting and clusterization of different data //Communications in Nonlinear Science and Numerical Simulation, Volume 19, Issue 12, December 2014, Pages 4080–4093	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=85554	ru_RU
dc.description.abstract	Communications in Nonlinear Science and Numerical Simulation	ru_RU
dc.description.abstract	Many experimentalists were accustomed to think that any independent measurement forms a non-correlated measurement that depends weakly from others. We are trying to reconsider this conventional point of view and prove that similar measurements form a strongly-correlated sequence of random functions with memory. In other words, successive measurements ?remember? each other at least their nearest neighbors. This observation and justification on real data help to fit the wide set of data based on the Prony?s function. The Prony?s decomposition follows from the quasi-periodic (QP) properties of the measured functions and includes the Fourier transform as a partial case. New type of decomposition helps to obtain a specific amplitude?frequency response (AFR) of the measured (random) functions analyzed and each random function contains less number of the fitting parameters in comparison with its number of initial data points. Actually, the calculated AFR can be considered as the generalized	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	The repeated EPR measurements	ru_RU
dc.subject	The quasi-periodic (QP) process	ru_RU
dc.subject	The generalized Prony?s spectrum (GPS)	ru_RU
dc.title	Detection of quasi-periodic processes in repeated measurements: new approach for the fitting and clusterization of different data	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU