Display Abstract

Title Uniform persistence for monotone skew-product semiflows with applications to neural networks

Name Sylvia Novo
Country Spain
Email sylnov@wmatem.eis.uva.es
Co-Author(s) Rafael Obaya and Ana M. Sanz
Submit Time 2014-02-26 14:30:41
Session
Special Session 9: Dissipative systems and applications
Contents
Uniform persistence and other related notions have been extensively studied for autonomous and non-autonomous dynamical systems. We introduce a concept of uniform persistence above and below a minimal set of an abstract monotone skew-product semiflow. When the minimal set has a continuous separation we characterize the uniform persistence in terms of the principal spectrum. Applications to the case in which the flow is generated by the solutions of a family of non-autonomous functional differential equations will be shown, as well as a method for the calculus of the upper Lyapunov exponent of the minimal set. Finally, we will illustrate our theory with the study of the behavior of a family of almost periodic neural networks, in a situation where persistence actually implies permanence.