Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances
研究了带时变延迟和有界扰动的忆阻神经网络的可达集估计问题，提出了两种新的代数判据来估计零初始和非零初始条件下的可达集，结论易于验证且更精确。
This brief discusses the reachable set estimation (RSE) problem for memristive neural networks (MNNs) involving time-varying delays and bounded disturbances. The reachable sets of the considered MNNs under zero and nonzero initial conditions are estimated by two novel algebraic criteria, respectively. Compared with the existing results, the conclusions are easy to verify, and the obtained reachable sets are more accurate. Finally, the validity of the theoretical results is illustrated by two examples.