Replication of Chaos in Neural Networks Economics and Physics 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. 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.