Announcement of Report: A.V. Makarenko, Deep Convolutional Neural Networks for Chaos Identification in Signal Processing
This paper demonstrates effective capabilities of a relatively simple deep convolutional neural network in estimating the Lyapunov exponent and detecting chaotic signals. A major difference between this study and existing research is that our networks take raw data as input, automatically generate a selection of informative features, make a direct estimation of the Lyapunov exponent and form a decision whether a chaotic signal is present. This process does not involve attractor reconstruction, and the estimator works with rather short signals~$1024$ sequence elements in this experiment. The study has demonstrated that deep convolutional neural networks are effective in applications involving chaotic signals (down to narrowband or broadband stochastic processes), as well as distinct patterns, and can, therefore, be used for a number of signal processing tasks.
The report was accepted for presentation at 26th European Signal Processing Conference (EUSIPCO 2018), Italy, Rome, on September 3–7, 2018. The conference is held under auspices of the European Association for Signal Processing and the IEEE Signal Processing Society. Read more: www.eusipco2018.org.
18 May 2018.
News Source: Own information.