Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863068 | Neural Networks | 2018 | 12 Pages |
Abstract
This paper presents a new theoretical design of nonlinear optimal control on achieving chaotic synchronization for coupled stochastic neural networks. To obtain an optimal control law, the proposed approach is developed rigorously by using Hamilton-Jacobi-Bellman (HJB) equation, Lyapunov technique, and inverse optimality, and hence guarantees that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals. Furthermore, the paper provides four numerical examples to demonstrate the effectiveness of the proposed approach.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ziqian Liu,