When machine learning meets fractional-order chaotic signals: detecting dynamical variations

dc.authorid0000-0003-2651-5005en_US
dc.contributor.authorKavuran, Gürkan
dc.date.accessioned2022-04-25T10:26:22Z
dc.date.available2022-04-25T10:26:22Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe challenge of classifying multivariate time series generated by discrete and continuous dynamical systems according to their chaotic or non-chaotic behavior has been studied extensively in the literature. The examination of noise or the variation of variables that affect a dynamic system's chaoticity will not be beneficial in analyzing structures employing random number generators (RNG) that are already assured to be chaotic. However, detecting the structural changes and their time intervals in deterministic systems with proven chaoticity can contribute to the literature in encryption applications. Machine Learning algorithms provide flexible possibilities to analyze and predict manipulations that may occur in the dynamics of chaotic and complex systems. This study proposes a deep Long-Short-Term-Memory (LSTM) network with a classification process to predict dynamical changes in a fractional-order chaotic (FOC) system. First, the appropriate system parameters are calculated to satisfy the chaotic behavior in the fractional-order Chen system. The predictive-corrective Adams-Bashforth-Moulton algorithm is used to simulate the FOC Chen system in the time domain. The Lyapunov exponents of the system were obtained according to the Wolf method. Next, three different scenarios have been designed to test and demonstrate the effectiveness of the proposed method. Synthetic FOC signals obtained after sub-sampling and statistical feature extraction processes fed the input of the deep bidirectional LSTM (BiLSTM) network to perform the training and testing process. The classification performance for "q" and "c" classes reaches 100% with the proposed model. The overall average testing accuracy, sensitivity, specificity, precision, F1 score and MCC are 98%, 98%, 99.3%, 98.1%, 98%, and 97.3%, respectively. Our results demonstrate the utility of using a deep BiLSTM network for detecting dynamical variations in nonlinear FOC systems.en_US
dc.identifier.citationKavuran, G. (2022). When machine learning meets fractional-order chaotic signals: detecting dynamical variations. Chaos, Solitons & Fractals, 157, 111908.en_US
dc.identifier.doi10.1016/j.chaos.2022.111908
dc.identifier.endpage13en_US
dc.identifier.issn0960-0779en_US
dc.identifier.issue111908en_US
dc.identifier.scopus2-s2.0-85124807914en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.chaos.2022.111908
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1032
dc.identifier.volume157en_US
dc.identifier.wosWOS:000784297500012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKavuran, Gürkan
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofChaos, Solitons and Fractalsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiLSTMen_US
dc.subjectChaosen_US
dc.subjectClassificationen_US
dc.subjectDeep learningen_US
dc.subjectFractional-order dynamical systemsen_US
dc.subjectTime seriesen_US
dc.titleWhen machine learning meets fractional-order chaotic signals: detecting dynamical variationsen_US
dc.typeArticleen_US

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