| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4946928 | Neurocomputing | 2017 | 5 Pages |
Abstract
This paper is concerned with the stochastic stabilization for genetic regulatory networks. Based on the Lyapunov stability theory in combination with certain convex algorithm, we obtain the sufficient condition under which the unstable genetic regulatory network can be stabilized by using Brownian motion. Finally, a numerical illustrative example is provided to show the effectiveness and correctness of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qi Luo, Lili Shi, Yutian Zhang,