Deep learning based classification of time series of Chen and Rössler chaotic systems over their graphic images
In this study, the graphic images of time series of different chaotic systems are classified with
deep learning methods for the first time in the literature. For the classification, a
datasetcontains images of time series of Chen and Rossler chaotic systems for different
parameter values, initial conditions, step size and time length are generated. Then, high
accuracy classifications are performed with transfer learning methods. The used transfer
learning methods are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet-101, DenseNet …
deep learning methods for the first time in the literature. For the classification, a
datasetcontains images of time series of Chen and Rossler chaotic systems for different
parameter values, initial conditions, step size and time length are generated. Then, high
accuracy classifications are performed with transfer learning methods. The used transfer
learning methods are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet-101, DenseNet …
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