A chaotic two-dimensional image classification algorithm based on convolutional neural networks

Xuefang Zhou

doi:10.1016/j.chaos.2025.116291

A chaotic two-dimensional image classification algorithm based on convolutional neural networks

2025-06-01

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DOI：10.1016/j.chaos.2025.116291

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To evaluate deep learning's classification capabilities on extensive datasets and its noise tolerance, a chaotic twodimensional (2D) image classification algorithm using a convolutional neural network is introduced. 1D time series from circuit and laser chaotic systems are transformed into 128 x 128 grayscale images to create a diverse dataset. A four-layer convolutional neural network (CNN) then classifies seven types of chaos, then results show that the system achieves an accurate chaos classification rate of up to 99.56 %. This paper also examines the impact of network depth, noise intensity, and image pixel size on performance. With a four-layer network, the system shows higher accuracy and lower performance loss. Adding Gaussian noise with a variance of 0.1 still maintains accuracy above 98.5 %. Increasing image resolution to 128 x 128 pixels results in a stable accuracy of 99.3 %. Overall, the system demonstrates strong robustness and generalization ability.

Keywords:

Deep learning

Chaotic sequences

Chaotic classification

2D images

A chaotic two-dimensional image classification algorithm based on convolutional neural networks

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