| Grant number: | 15/23487-8 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | May 01, 2016 |
| End date: | July 31, 2016 |
| Field of knowledge: | Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls |
| Principal Investigator: | Elbert Einstein Nehrer Macau |
| Grantee: | Thiago de Lima Prado |
| Host Institution: | Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovação (Brasil). São José dos Campos , SP, Brazil |
| Associated research grant: | 11/50151-0 - Dynamical phenomena in complex networks: fundamentals and applications, AP.TEM |
Abstract Many are the dynamical systems in which the stationary can not be well dened atnite time, and for real dynamical systems this is almost a rule due seldom possibilityto dene and reach this state. With this background, it is necessary to evaluate thissystems using tools that takes this characteristics in account, and can be implementedeven under complex situations as such climate analysis (with few statistics on data) orneuronal signal (which the acquisition receives a lot of noise and to repeat many times thecollection of data is not normally possible). Some of the most important tools availableand with simple application to such problems is derived from the recurrence concept ofPoincare, which one of many possible approach is through recurrence plots (RPs), thatgenerates a matrix with zeros and ones describing the recurrence of the dynamical systemevolution. Many are the recurrence quantiers which can be extracted from the plots,among them the determinism (DET) is based in the diagonal lines formed in a given RP,and it is generally related with the predictability of the system.This tool (and others basedin similar concepts) showed to be quite useful to distinguish transitions of dierent kinds(as such periodic-chaos, chaos-chaos, chaos-hyperchaos among others) for many dierentdynamical systems. In this proposal of work, we aim to show how a rened analysis withrecurrence framework can be extremely useful to increase the understanding of climaticphenomena, coupled dynamical and neuronal systems, based upon our previous experiencein non stationary systems. | |
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