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Deep learning based classification of time series of chaotic systems over graphic images

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Abstract

In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, 54050, Sakarya, Turkey

    Süleyman UZUN

  2. Department of Electrical and Electronics Engineering, Faculty of Technology, Sakarya University of Applied Sciences, 54050, Sakarya, Turkey

    Sezgin Kaçar & Burak Arıcıoğlu

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  1. Süleyman UZUN
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S. UZUN, S. KAÇAR and B. ARICIOĞLU contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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UZUN, S., Kaçar, S. & Arıcıoğlu, B. Deep learning based classification of time series of chaotic systems over graphic images. Multimed Tools Appl 83, 8413–8437 (2024). https://doi.org/10.1007/s11042-023-15944-3

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