Name
Title | ||
|---|---|---|
Multistability and complete convergence analysis on high-order neural networks with a class of nonsmooth activation functions |
Abstract | ||
|---|---|---|
In this paper, we are concerned with a class of high-order neural networks (HONNs) with nonsmooth activation functions. A set of new sufficient conditions ensuring the coexistence of 3 n equilibrium points and the local stability of 2 n equilibrium points are proposed, which reveal that the high-order interactions between neurons also play an important role on the multistability of HONNs. Besides, every solution is shown to converge to a certain equilibrium point, that is, the systems are also completely stable. Furthermore, for the 2-neuron neural networks, we can get that the stable manifolds of unstable equilibrium points constitute the boundaries of attraction basins of stable equilibrium points, despite the nonlinearity of high-order items of HONNs. Several numerical examples are presented to illustrate the effectiveness of our criteria. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.10.075 | Neurocomputing |
Keywords | Field | DocType |
High-order neural networks,Multistability,Nonsmooth activation function,Complete stability | Convergence (routing),Applied mathematics,Mathematical optimization,Nonlinear system,Pattern recognition,Equilibrium point,Artificial intelligence,Stable equilibrium,Multistability,Artificial neural network,Manifold,Mathematics | Journal |
Volume | Issue | ISSN |
152 | C | 0925-2312 |
Citations | PageRank | References |
7 | 0.44 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lili Wang | 1 | 269 | 13.90 |
| Tianping Chen | 2 | 3095 | 250.77 |