Abstract
Multistability is an important property of recurrent neural networks. It plays a crucial role in some applications, such as decision making, association memory, etc. This paper studies multistability of a class of neural networks with different time scales under the assumption that the activation functions are unsaturated piecewise linear functions. Using local inhibition to the synaptic weights of the networks, it is shown that the trajectories of the network are bounded. A global attractive set which may contain multi-equilibrium points is obtained. Complete convergence is proved by constructing an energy-like function. Simulations are employed to illustrate the theory.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
