Multiperiodicity of Periodically Oscillated Discrete-Time Neural Networks with Transient Excitatory Self-Connections and Sigmoidal Nonlinearities

作者：Huang, Zhenkun^*; Wang, Xinghua; Feng, Chunhua

来源：IEEE Transactions on Neural Networks, 2010, 21(10): 1643-1655.

DOI：10.1109/TNN.2010.2067225

• Discrete time
• excitatory self-interactions
• multiperiodicity
• neural networks
• sigmoidal nonlinearities

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.

• 出版日期2010-10
• 单位广西师范大学; 浙江大学; 集美大学