title: Periodicity and dissipativity for memristor-based mixed time-varying delayed neural networks via differential inclusions.
abstract: In this paper, we investigate a class of memristor-based neural networks with general mixed delays involving both time-varying delays and distributed delays. By using the Mawhin-like coincidence theorem, together with the differential inclusion theory, M-matrix properties and differential inequality techniques, some novel criteria are established for ensuring the periodicity and dissipativity for the addressed neural networks. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results.
authors.affiliation: College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China. Electronic address: lianduan0906@163.com.
authors.forename: Lian
authors.initials: L
authors.lastname: Duan
authors.affiliation: College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China; Department of Information Technology, Hunan Women's University, Changsha, Hunan 410004, PR China. Electronic address: huanglihong1234@126.com.
authors.forename: Lihong
authors.initials: L
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D010507:Periodicity
keywords: Dissipativity; Mawhin-like coincidence theorem; Memristor-based neural networks; Periodicity