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"NASA Technical Reports Server (NTRS) 19950016571: Lyapunov Exponents From CHUA's Circuit Time Series Using Artificial Neural Networks" and the language of the book is English.


“NASA Technical Reports Server (NTRS) 19950016571: Lyapunov Exponents From CHUA's Circuit Time Series Using Artificial Neural Networks” Metadata:

  • Title: ➤  NASA Technical Reports Server (NTRS) 19950016571: Lyapunov Exponents From CHUA's Circuit Time Series Using Artificial Neural Networks
  • Author: ➤  
  • Language: English

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  • Internet Archive ID: NASA_NTRS_Archive_19950016571

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"NASA Technical Reports Server (NTRS) 19950016571: Lyapunov Exponents From CHUA's Circuit Time Series Using Artificial Neural Networks" Description:

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In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

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