Non-linear Correlation Based Approach to the Identification of Maximally Stationary Systems
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 364 LNNS, Page: 209-218
2022
- 3Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations3
- Citation Indexes3
- CrossRef2
Conference Paper Description
An approach to the identification of non-linear maximally stationary systems is proposed, based on the use of a consistent measure of dependence of the input and output processes of the system under study. In accordance with the conventional terminology, a measure of dependence between two random values (processes) is referred to as consistent, if it vanishes if and only if the values (processes) are stochastically independent. Within the consideration subject, such a measure of dependence is the maximal correlation. In turn, the maximally stationary systems are those, for which the first eigenfunctions, corresponding to the largest in the absolute value first eigenvalue of the joint probability distribution density expansion, do not depend on the time.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85121783838&origin=inward; http://dx.doi.org/10.1007/978-3-030-92604-5_19; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85121749289&origin=inward; https://link.springer.com/10.1007/978-3-030-92604-5_19; https://dx.doi.org/10.1007/978-3-030-92604-5_19; https://link.springer.com/chapter/10.1007/978-3-030-92604-5_19
Springer Science and Business Media LLC
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