Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4946797 | Neural Networks | 2017 | 14 Pages |
Abstract
This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3k equilibrium points with 0â¤kâ¤n, among which 2k and 3kâ2k equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Peng Liu, Zhigang Zeng, Jun Wang,