Multi-chaotic signal identification employing a causal cross-correlation neural network.

Authors

Bingrui Wang, Xinyang Piao, Chengbin Chen, Ente Guo, Xingang Zhang

Published in

iScience. Volume 29. Issue 7. Pages 116164. Jul 17, 2026. Epub Jun 26, 2026.

Abstract

Chaos identification plays a crucial role in comprehending complex systems. However, current methods face three challenges: neglect temporal causality, lack robustness against noise, and identify a limited number of chaotic signals. To address the challenges, we focus on identifying multi-chaotic signals with noise interference. First, this study examines 15 types of chaos models. We propose an acquisition-enhanced Bayesian optimization to improve the Swish-modulo map, which is then verified using the maximum Lyapunov exponent. Second, we present a causal cross-correlation network for chaos identification, which incorporates a learnable wavelet transform, time-frequency feature fusion, and spectral-inversion-free fast Fourier transform-based cross-correlation (SFFT-CC). Consequently, experimental results show that the chaos identification accuracy reaches 96.67% at a 20 dB noise. The SFFT-CC outperforms the traditional dilated convolution by up to 5.42% in chaos identification accuracy, while achieving a 43-fold reduction in training time. Theoretical analyses are provided to elucidate these results.

PMID:
42396408
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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