Journal
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
Volume 5, Issue 3, Pages 290-295Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11633-008-0290-x
Keywords
Neural network; dissipativity; Lyapunov functional
Funding
- National Natural Science Foundation of China [60674026]
- Key Project of Chinese Ministry of Education [107058]
- Jiangsu Provincial Natural Science Foundation of China [BK2007016]
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A new definition of dissipativity for neural networks is presented in this paper. By constructing proper Lyapunov functionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.
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