Basic Concepts of Chaos Theory and Nonlinear Time-Series Analysis
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-94482-7_2
Reference24 articles.
1. Alligood, K. T., Sauer, T. D., & Yorke, J. A. (1996). Chaos. Springer.
2. Boeing, G. (2016). Visual analysis of nonlinear dynamical systems: Chaos, fractals, self-similarity and the limits of prediction. Systems, 4.4, 37.
3. Briggs, K. (1990). An improved method for estimating Liapunov exponents of chaotic time series. Physics Letters A, 151.1-2, 27–32.
4. Brown, R., Bryant, P., & Abarbanel, H. D. (1991). Computing the Lyapunov spectrum of a dynamical system from an observed time series. Physical Review A, 43.6, 2787.
5. Dercole, F., Sangiorgio, M., & Schmirander, Y. (2020). An empirical assessment of the universality of ANNs to predict oscillatory time series. IFAC-PapersOnLine, 53.2, 1255–1260.
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