title: Multiperiodicity of periodically oscillated discrete-time neural networks with transient excitatory self-connections and sigmoidal nonlinearities.
abstract: The existing approaches to the multistability and multiperiodicity of neural networks rely on the strictly excitatory self-interactions of neurons or require constant interconnection weights. For periodically oscillated discrete-time neural networks (DTNNs), it is difficult to discuss multistable dynamics when the connection weights are periodically oscillated around zero. By using transient excitatory self-interactions of neurons and sigmoidal nonlinearities, we develop an approach to investigate multiperiodicity and attractivity of periodically oscillated DTNNs with time-varying and distributed delays. It shows that, under some new criteria, there exist multiplicity results of periodic solutions which are locally or globally exponentially stable. Computer numerical simulations are performed to illustrate the new theories.
authors.affiliation: School of Sciences, Jimei University, Xiamen 361021, China. hzk974226@jmu.edu.cn
authors.forename: Zhenkun
authors.initials: Z
authors.lastname: Huang
authors.forename: Xinghua
authors.initials: X
authors.lastname: Wang
authors.forename: Chunhua
authors.initials: C
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010507:Periodicity