Replication of chaos in neural networks, economics and physics
- 作者： Akhmet, Marat, author.
- 其他作者：
- 其他題名：
- Nonlinear physical science.
- 出版： Berlin, Heidelberg : Springer Berlin Heidelberg :Imprint: Springer
- 叢書名： Nonlinear physical science,
- 主題： Chaotic behavior in systems. , Topological dynamics. , Physics. , Nonlinear Dynamics. , Difference and Functional Equations. , Theoretical and Applied Mechanics. , Game Theory/Mathematical Methods. , Mathematical and Computational Biology.
- ISBN： 9783662475003 (electronic bk.) 、 9783662474990 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction -- Replication of Continuous Chaos about Equilibria -- Chaos Extension in Hyperbolic Systems -- Entrainment by Chaos -- Chaotification of Impulsive Systems by Perturbations -- Chaos Generation in Continuous/Discrete-Time Models -- Economic Models with Deterministic Chaos as Generated by Exogenous Continuous/Discrete Shocks -- Replication of Chaos by Neural Networks -- ntrainment by Spatiotemporal Chaos in Glow Discharge-Semiconductor Systems.
- 摘要註: 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 types of chaos. The appearance of deterministic chaos in neural networks, economics and mechanical systems is discussed theoretically and supported by simulations. As such, the book offers a valuable resource for mathematicians, physicists, engineers and economists studying nonlinear chaotic dynamics.
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讀者標籤：
- 系統號： 005357391 | 機讀編目格式
館藏資訊
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 types of chaos. The appearance of deterministic chaos in neural networks, economics and mechanical systems is discussed theoretically and supported by simulations. As such, the book offers a valuable resource for mathematicians, physicists, engineers and economists studying nonlinear chaotic dynamics.