期刊
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
卷 222, 期 2, 页码 440-457出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cam.2007.11.015
关键词
Global robust synchronization; Neural networks; Lyapunov functional; Parameter identification; Output coupling; State coupling
资金
- NSFC [60674026]
- Key Research Foundation of Science and Technology of the Ministry of Education of China [107058]
- National Natural Science Fundation of Jiangsu Province [BK2007016]
- Jiangsu Provincial Program for Postgraduate Scientific Innovative Research of Jiangnan University [CX07B_116z]
- PIRTJiangnan
For neural networks with all the parameters unknown, we focus on the global robust synchronization between two coupled neural networks with time-varying delay that are linearly and unidirectionally coupled. First, we use Lyapunov functionals to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global robust synchronization regardless of their initial states. Second, by employing the invariance principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the robust synchronization of almost all kinds of coupled neural networks with time-varying delay based on the parameter identification of uncertain delayed neural networks. Finally, numerical simulations validate the effectiveness and feasibility of the proposed technique. (c) 2007 Elsevier B.V. All rights reserved.
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