Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4946960 | Neurocomputing | 2017 | 26 Pages |
Abstract
Passivity of memristor-based recurrent neural networks (MRNNs) with multi-proportional delays is investigated in this paper. Here, proportional delay is an unbounded time-varying delay, which is distinct from constant delay, bounded time-varying delay and distributed delay. In the sense of Filippov solution, we present several new sufficient conditions for the passivity of MRNNs with multi-proportional delays, which are delay-independent and delay-dependent, by establishing appropriate Lyapunov functionals and utilizing inequality techniques. The passivity criteria here are presented in the form of linear matrix inequalities (LMIs). Finally, a numerical example and its simulations are given to illustrate the accuracy and validation of the obtained results.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lijuan Su, Liqun Zhou,