COVID-19 and maternal and perinatal outcomes

Chmielewska B., Barratt I., Townsend R., Kalafat E., van der Meulen J., Gurol-Urganci I., ...Daha Fazla

LANCET GLOBAL HEALTH, cilt.9, sa.8, 2021 (SCI-Expanded) identifier identifier identifier identifier

  • Yayın Türü: Makale / Kısa Makale
  • Cilt numarası: 9 Sayı: 8
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/s2214-109x(21)00302-8
  • Dergi Adı: LANCET GLOBAL HEALTH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, CAB Abstracts, EMBASE, Food Science & Technology Abstracts, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

This paper aims to theoretically prove by applying Marotto's Theorem that both transiently chaotic neural networks (TCNN) and discrete-time recurrent neural networks (DRNN) have chaotic structure. A significant property of TCNN and DRNN is that they have only one fixed point, when absolute values of the self-feedback connection weights in TCNN and the difference time in DRNN are sufficiently large. We show that this unique fixed point can actually evolve into a snap-back repeller which generates chaotic structure, if several conditions are satisfied. On the other hand, by using the Lyapunov functions, we also derive sufficient conditions on asymptotical stability for symmetrical versions of both TCNN and DRNN, under which TCNN and DRNN asymptotically converge to a fixed point. Furthermore, generic bifurcations are also considered in this paper. Since both of TCNN and DRNN are not special but simple and general, the obtained theoretical results hold for a wide class of discrete-time neural networks. To demonstrate the theoretical results of this paper better, several numerical simulations are provided as illustrating examples.