Deep learning-based classification of chaotic systems over phase portraits
نویسندگان
چکیده
This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best authors' knowledge, such studies portraits have not been conducted in literature. that end, dataset consisting most known two systems, namely Lorenz and Chen, is generated for different values parameters, initial conditions, step size, time length. Then, with high accuracy carried out employing transfer learning methods. The methods used are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, GoogLeNet models. As result study, between 97.4% 100% 2-ways classifier 83.68% 99.82% 3-ways achieved depending on problem. Thanks to this, random signals obtained real life can be associated mathematical model.
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ژورنال
عنوان ژورنال: Turkish Journal of Electrical Engineering and Computer Sciences
سال: 2023
ISSN: ['1300-0632', '1303-6203']
DOI: https://doi.org/10.55730/1300-0632.3969