Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching. (January 2023)

• Record Type: Journal Article
• Authors: Ci, Jingxuan; Guo, Zhenyuan; Long, Han; Wen, Shiping; Huang, Tingwen
• Is Part Of: Neural networks. Volume 157(2023)
• Page Start: 11
• Page End: 25
• Publication Date: 2023-01
• Subjects: Fractional-order neural network -- Multiple asymptotical ω-periodicity -- Delay -- State-dependent switching; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.09.034
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents theoretical results on multiple asymptotical ω -periodicity of a state-dependent switching fractional-order neural network with time delays and sigmoidal activation functions. Firstly, by combining the geometrical properties of activation functions with the range of switching threshold, a partition of state space is given. Then, the conditions guaranteeing that the solutions can approach each other infinitely in each positive invariant set are derived. Furthermore, the S -asymptotical ω -periodicity and the convergence of solutions in positive invariant sets are discussed. It is worth noting that the number of attractors increases to 3 n from 2 n in a neural network without switching. Finally, three numerical examples are given to substantiate the theoretical results. Highlights: Proposed a switched fractional-order neural network model. Proved the S-asymptotical ω -periodicity of solutions. Investigated the multiple asymptotical ω -periodicity of switching system.