- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly (4k + 3)k0 equilibria, (2k + 2)k0 of which are locally exponentially stable and the others are unstable, where k0 is a nonnegative integer such that k0 ≤ n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results.
In this paper, we have discussed the multistability and instability issue of delayed recurrent neural networks. By the division of state space and the dimensional space reconstruction, some sufficient criteria have been established to ensure the existence of (2k + 2)k0 locally exponentially stable equilibria, and (4k + 3)k0 − (2k+2)k0 equilibria are unstable, where k0 is a nonnegative integer such that k0 ≤ n. These new criteria improve and extend the existing results of multistability in the literature. By means of coupling division, it also reveals that the different regions of parameter are influenced by division, and these regions of parameter are allowed to have more options, in which the dynamic behaviors are more abundant. Finally, a numerical simulation is conducted to illustrate the derived theoretical results.