Theory of Translation Closedness for Time Scales

Chao Wang|Ravi P. Agarwal|Donal O\' Regan|Rathinasamy Sakthivel

ISBN 10: 3030386430 / ISBN 13: 9783030386436

Verlag: Springer International Publishing, 2020

Beschreibung

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Establishes a theory of classification and translation closedness of time scalesExplores its use in practical model scenarios, like Nicholson`s blowfiles model, the Lasota-Wazewska model, the Keynesian-Cross model and others.

Bestandsnummer des Verkäufers 448680987

Bibliografische Details

• Titel: Theory of Translation Closedness for Time Scales
• Verlag: Springer International Publishing
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 3030386430
• ISBN-13: 9783030386436
• Einband: Gebunden
• Zustand: New
• Verkäuferkataloge:
  • Mathematik/Naturwissenschaften/Technik/Medizin

Über diesen Titel

Inhaltsangabe

This monograph establishes a theory of classification and translation closedness of time scales, a topic that was first studied by S. Hilger in 1988 to unify continuous and discrete analysis. The authors develop a theory of translation function on time scales that contains (piecewise) almost periodic functions, (piecewise) almost automorphic functions and their related generalization functions (e.g., pseudo almost periodic functions, weighted pseudo almost automorphic functions, and more). Against the background of dynamic equations, these function theories on time scales are applied to study the dynamical behavior of solutions for various types of dynamic equations on hybrid domains, including evolution equations, discontinuous equations and impulsive integro-differential equations.

The theory presented allows many useful applications, such as in the Nicholson`s blowfiles model; the Lasota-Wazewska model; the Keynesian-Cross model; in those realistic dynamical models with a more complex hibrid domain, considered under different types of translation closedness of time scales; and in dynamic equations on mathematical models which cover neural networks. This book provides readers with the theoretical background necessary for accurate mathematical modeling in physics, chemical technology, population dynamics, biotechnology and economics, neural networks, and social sciences.

Über die Autorin bzw. den Autor

Chao Wang is an associate professor and doctor in mathematics and teaches at the department of mathematics in Yunnan University in China. His research focuses on the  fields of nonlinear dynamic systems, control theory, fuzzy dynamic equations, fractional differential equations, bifurcation theory, nonlinear analysis and numerical modeling.
Ravi P. Agarwal is a Professor at the Texas A&M University in Kingsville, USA. He is also a Distinguished University Professor of Mathematics at the Florida Institute of Technology, Melbourne, FL, USA. He completed his PhD at the Indian Institute of Technology, India. Dr.  Agarwal has authored, co-authored or co-edited over 60 books, including  "An Introduction to Ordinary Differential Equations" (978-9-387-71275-8) and "Ordinary and Partial Differential Equations" (978-0-387-79145-6), both co-authored by Donal O`Regan and published by Springer.
Donal O'Regan is a Professor at the School of Mathematics of the National University of Ireland. His research interests are in nonlinear functional analysis. His previous publications with Springer include "Constant-Sign Solutions of Systems of Integral Equations" (978-3-319-01254-4) and "Fixed Point Theory for Lipschitzian-type Mappings with Applications" (978-0-387-75817-6), both as a co-author.
Rathinasamy Sakthivel is a Professor at the Department of Applied Mathematics, Bharathiar University, India. He completed his Ph.D. at Bharathiar University, India. His research focuses on the fields of systems and control theory, differential and integral equations, fractional differential equations and numerical methods for PDEs.  He is an active editorial board member of several journals, including IEEE Access, Journal of the Franklin Institute, Neurocomputing, Journal of Electrical Engineer.