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

Author

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  • Xuefang, Zhou
  • Hongliang, Wang
  • Junchao, Hu
  • Li, Haozhen
  • Xu, Mengmeng
  • Miao, Hu

Abstract

To evaluate deep learning's classification capabilities on extensive datasets and its noise tolerance, a chaotic two-dimensional (2D) image classification algorithm using a convolutional neural network is introduced. 1D time series from circuit and laser chaotic systems are transformed into 128 × 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 × 128 pixels results in a stable accuracy of 99.3 %. Overall, the system demonstrates strong robustness and generalization ability.

Suggested Citation

  • Xuefang, Zhou & Hongliang, Wang & Junchao, Hu & Li, Haozhen & Xu, Mengmeng & Miao, Hu, 2025. "A chaotic two-dimensional image classification algorithm based on convolutional neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:chsofr:v:195:y:2025:i:c:s0960077925003042
    DOI: 10.1016/j.chaos.2025.116291
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