The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
현재 장기 기억 행동은 확률론적 과정과 연관되어 있습니다. FARIMA 프로세스와 같은 다양한 모델로 모델링할 수 있습니다. k- GARMA 프로세스 또는 프랙탈 브라운 운동을 고려합니다. 반면, 초기 조건에 대한 민감성과 어트랙터의 존재를 특징으로 하는 혼돈 시스템은 일반적으로 그 동작이 무작위 백색 잡음에 가까운 것으로 가정됩니다. 여기서 우리는 장기 기억 프로세스를 차원 1 또는 상위 차원에 정의된 잘 알려진 혼돈 시스템에 맞게 조정할 수 있는 이유를 보여줍니다. 이 새로운 접근 방식을 사용하면 혼돈 시스템과 관련된 불변 측정을 다른 방식으로 특성화하고 장기 예측 방법을 제안할 수 있습니다. 즉, 많은 적용 분야에서 응용 프로그램을 찾는 두 가지 속성입니다.
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Dominique GUEGAN, "Long Memory Behavior for Simulated Chaotic Time Series" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 9, pp. 2145-2154, September 2001, doi: .
Abstract: Currently the long memory behavior is associated to stochastic processes. It can be modeled by different models such like the FARIMA processes, the k-factors GARMA processes or the fractal Brownian motion. On the other side, chaotic systems characterized by sensitivity to initial conditions and existence of an attractor are generally assumed to be close in their behavior to random white noise. Here we show why we can adjust a long memory process to well known chaotic systems defined in dimension one or in higher dimension. Using this new approach permits to characterize in another way the invariant measures associated to chaotic systems and to propose a way to make long term predictions: two properties which find applications in a lot of applied fields.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_9_2145/_p
부
@ARTICLE{e84-a_9_2145,
author={Dominique GUEGAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Long Memory Behavior for Simulated Chaotic Time Series},
year={2001},
volume={E84-A},
number={9},
pages={2145-2154},
abstract={Currently the long memory behavior is associated to stochastic processes. It can be modeled by different models such like the FARIMA processes, the k-factors GARMA processes or the fractal Brownian motion. On the other side, chaotic systems characterized by sensitivity to initial conditions and existence of an attractor are generally assumed to be close in their behavior to random white noise. Here we show why we can adjust a long memory process to well known chaotic systems defined in dimension one or in higher dimension. Using this new approach permits to characterize in another way the invariant measures associated to chaotic systems and to propose a way to make long term predictions: two properties which find applications in a lot of applied fields.},
keywords={},
doi={},
ISSN={},
month={September},}
부
TY - JOUR
TI - Long Memory Behavior for Simulated Chaotic Time Series
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2145
EP - 2154
AU - Dominique GUEGAN
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2001
AB - Currently the long memory behavior is associated to stochastic processes. It can be modeled by different models such like the FARIMA processes, the k-factors GARMA processes or the fractal Brownian motion. On the other side, chaotic systems characterized by sensitivity to initial conditions and existence of an attractor are generally assumed to be close in their behavior to random white noise. Here we show why we can adjust a long memory process to well known chaotic systems defined in dimension one or in higher dimension. Using this new approach permits to characterize in another way the invariant measures associated to chaotic systems and to propose a way to make long term predictions: two properties which find applications in a lot of applied fields.
ER -