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Replication of Chaos in Neural Networks, Economics and Physics Introduction
Date
2016-01-01
Author
Akhmet, Marat
Fen, Mehmet Onur
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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URI
https://hdl.handle.net/11511/99173
Journal
REPLICATION OF CHAOS IN NEURAL NETWORKS, ECONOMICS AND PHYSICS
DOI
https://doi.org/10.1007/978-3-662-47500-3_1
Collections
Department of Mathematics, Article
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Replication of Chaos in Neural Networks, Economics and Physics Preface
Akhmet, Marat; Fen, Mehmet Onur (2016-01-01)
Replication of chaos in neural networks, economics and physics
Akhmet, Marat (Springer, London/Berlin , 2016-12-01)
This book presents detailed descriptions of chaos for continuous-time systems. It is the first-ever book to consider chaos as an input for differential and hybrid equations. Chaotic sets and chaotic functions are used as inputs for systems with attractors: equilibrium points, cycles and tori. The findings strongly suggest that chaos theory can proceed from the theory of differential equations to a higher level than previously thought. The approach selected is conducive to the in-depth analysis of different ...
Replication of chaos
Akhmet, Marat (Elsevier BV, 2013-10-01)
We propose a rigorous method for replication of chaos from a prior one to systems with large dimensions. Extension of the formal properties and features of a complex motion can be observed such that ingredients of chaos united as known types of chaos, Devaney's, Li-Yorke and obtained through period-doubling cascade. This is true for other appearances of chaos: intermittency, structure of the chaotic attractor, its fractal dimension, form of the bifurcation diagram, the spectra of Lyapunov exponents, etc. Th...
Replication of Continuous Chaos About Equilibria
Akhmet, Marat; Fen, Mehmet Onur (2016-01-01)
Inversion of linear time-invariant systems.
Emre, Erol; Department of Electrical Engineering (1974)
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M. Akhmet and M. O. Fen, “Replication of Chaos in Neural Networks, Economics and Physics Introduction,”
REPLICATION OF CHAOS IN NEURAL NETWORKS, ECONOMICS AND PHYSICS
, pp. 1–32, 2016, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99173.