A Complex Systems Approach To Feature Extraction For Chaotic Behavior Recognition

Pourafzal, Alireza; Fereidunian, Alireza

doi:10.1109/icspis51611.2020.9349551

Cited by 5 publications

(11 citation statements)

References 24 publications

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“…Here, we examine a highly narrow problem statement and a number of other model classes are proposed, for example, in [1][2][3][4][5]. In terms of the geometry of observation space, we consider it to be approximately flat.…”

Section: Methodsmentioning

confidence: 99%

“…In many practical problems, a complex oscillatory non-periodic process that contains local trends and jumps describes the change in the parameters of the observed system. The corresponding observation series usually implements chaotic processes [1][2][3][4][5]; examples include turbulent flows in unstable gas-dynamic and hydrodynamic environments [6][7][8], changes in asset prices at electronic capital markets [9][10][11], as well as many others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Multivariate Chaotic Processes with Precedent Analysis

2021

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Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precedent forecasting in which we use the aftereffects of retrospective observation segments that are similar to the current situation as a forecast. Furthermore, we employ the presence of relatively stable correlations between the parameters of the immersion environment as a regularizing factor. We pay special attention to the choice of similarity measures or distances used to find analog windows in arrays of retrospective multidimensional observations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Multivariate Chaotic Processes with Precedent Analysis

2021

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“…We realize that the number of trainable parameters in deep learning models (number of filters, kernels, and depth of the network) are chosen based on a trial-and-error process. However, as suggested by Pourafzal and Fereidunian [17] and Safarihamid et al [18], regardless of observation length of chaotic time series, they can be classified using a few features of the complex system [17,18]. This gives us the intuition that there is a feature space in which the given chaotic time series can be sparsified.…”

Section: Motivationmentioning

confidence: 97%

“…Moreover, approaches by Pourafzal and Fereidunian [17] and Safarihamid et al [18] took advantages of the link between complex systems and chaotic systems. According to Poincaré, the unpredictable behaviour of non-linear dynamical systems can be interpreted as an extreme point of complexity instead of disorder [19].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in this group of approaches, complex system features are utilized to detect chaotic time series. Pourafzal and Fereidunian [17] stated the most important features of complex systems as emergence, selforganization, predictability, and complexity. Then, information theoretic definitions of such characteristics were investigated and employed for chaos recognition.…”

Section: Introductionmentioning

confidence: 99%

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Chaotic Time Series Recognition: A Deep Learning Model Inspired by Complex Systems Characteristics

Pourafzal

Fereidunian

Safarihamid

2023

IJE

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A deep learning method is developed for chaotic time series classification. We investigate the chaotic state of a dynamical system, based on the output of the system. One of the main obstacles in time series classification is mapping a high-dimensional vector into a scalar value. To reduce the dimensions, it is common to use an average pooling layer block after feature extraction block. This blind process results in models with high computational complexity and potent to overfitting. One alternative is to extract the features manually, then apply shallow learning models to classify the time series. In fact, since complexity lies between the chaos and order, it is a sound idea to refer to complex systems characteristics to explore the chaotic region entrance. Therefore, chaotic state of a dynamical system can be recognized solely based on these characteristics. Inspired by this concept, we conclude that there is a feature space in which the output vector can be sparsified. Thus, we propose a deep learning method which the feature space dimensions successively are reduced in the feature extraction process. Specifically, we employ a fully convolutional network and add on two maximum pooling layers to the relevant feature extraction block. To validate the proposed model, the Lorenz system is employed which exhibits chaotic/nonchaotic states. We generate a labeled dataset containing 10000 samples each with 20000 features of the output of Lorenz system. The proposed model achieves 99.45 percent accuracy over 2000 unseen samples, higher than all the other competitor methods.

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Detecting emergence in engineered systems: A literature review and synthesis approach

Haugen

Skeie

Müller

et al. 2023

Systems Engineering

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Modern product development often generates systems of high complexity that are prone to emergent behavior. The industry has a need to establish better practices to detect inherent emergent behavior when engineering such systems. Philosophers and researchers have debated emergence throughout history, tracing to the time of the Greek philosopher Aristotle (384–322 B.C.) and current literature has both philosophical and practical examples of emergence in modern systems. In this review paper, we investigate the phenomenon of emergent behavior in engineered systems. Our aim is to describe emergence in engineered systems and propose methods to detect it, based on literature. Emergence is in general explained as dynamic behavior seen at macro level that cannot be traced back to the micro level. Emergence can be known or unknown in combination with positive or negative. We find that best practices to engineer complicated systems should contain a sensible suite of traditional approaches and methods, while best practices to engineer complex systems need extensions to this considering a new paradigm using incentives to guide system behavior rather than testing it up‐front.

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A Complex Systems Approach To Feature Extraction For Chaotic Behavior Recognition

Cited by 5 publications

References 24 publications

Forecasting Multivariate Chaotic Processes with Precedent Analysis

Forecasting Multivariate Chaotic Processes with Precedent Analysis

Chaotic Time Series Recognition: A Deep Learning Model Inspired by Complex Systems Characteristics

Detecting emergence in engineered systems: A literature review and synthesis approach

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