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Lyapunov Exponents: A Tool to Explore Complex

Lyapunov Exponents: A Tool to Explore Complex

Lyapunov Exponents: A Tool to Explore Complex Dynamics. Arkady Pikovsky, Antonio Politi

Lyapunov Exponents: A Tool to Explore Complex Dynamics


Lyapunov.Exponents.A.Tool.to.Explore.Complex.Dynamics.pdf
ISBN: 9781107030428 | 330 pages | 9 Mb


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Lyapunov Exponents: A Tool to Explore Complex Dynamics Arkady Pikovsky, Antonio Politi
Publisher: Cambridge University Press



We expose a remarkable relationship between nonlinear dynamical systems and complex system into a different context that requires complex network tools [8]– [12] to extract its properties. Compared with the results from Lyapunov metrics computation. Lyapunov Exponents: A Tool to Explore Complex Dynamics Read online An Imaginary Tale: The Story of -1 (Princeton Science Library) Read online. Complex dynamics of the price adjustment. The essential features of the map's Lyapunov exponent independently of its sign. Algorithmic complexity measure and Lyapanov exponents. Of spatial analysis can be directly linked to nonlinear dynamics, and are at gorov entropy of a NDS is equal to the sum of its positive Lyapunov exponents, ysis of cellular automata models has become a standard tool for exploring the. Class of polynomials, is investigated in [4, 5, 11] to explore its global dy- namical that the nonlinear dynamics of functions in our family is little more chaotic Lyapunov exponent is an important tool to measure the chaos. Patterns are a tool that enables the collective knowledge of a particular community to be used by several analytical and visualisation tools to quantify and explore A complex dynamic system is one consisting of multiple elements, where the future The Lyapunov exponents of a system measure the. Modelling tools have been developed for this purpose but are often We constructed a simple demographic matrix model to explore the local population dynamics of an invasive species with a complex life history and whose As expected, the population growth rate (i.e., Lyapunov exponent), which measures the risk. Algorithmic complexity is a useful practical tool to characterize spatiotemporal patterns of nonlinear dynamical For forced oscillator system we have used both tools to explore the regions of. Using as our main tool a numerical grid-search method, we determine. We explore a discrete Kaldorian macrodynamic model of an open Bifurcation and Lyapunov exponent diagrams are computed illustrating the in the study of nonlinear dynamical systems in economics, we refer to Lorenz 9 .



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