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    • Computing of Lyapunov Exponents Techniques Using Neural Networks 

      Golovko, Vladimir; Savitsky, Yury (BSUIR, 2003)
      The authors examine neural network techniques for computing of Lyapunov spectrum using observations from unknown dynamical system. Such an approach is based on applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the ...

      2023-11-17

    • Neural Networks for Chaotic Time Series Forecasting 

      Golovko, Vladimir; Savitsky, Yury (BSUIR, 2001)
      This paper examines neural network in order to predict behavior o f chaotic systems. The prediction is performed both on the level o f emergent structures and on the level o f individual data points. The network is tested using the Henon and Lorenz chaotic time series. The results o f experiments and ...

      2023-11-16

    • Some Aspects of Chaotic Time Series Analysis 

      Golovko, Vladimir; Savitsky, Yury; Maniakov, Nikolaj; Rubanov, Vladimir (BSUIR, 2001)
      We address two aspects in chaotic time series analysis, namely the definition of embedding parameters and the largest Lyapunov exponent. It is necessary for performing state space reconstruction and identification of chaotic behavior. For the first aspect, we examine the mutual information for ...

      2023-11-16