Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11633-008-0290-x | International Journal of Automation and Computing |
Keywords | Field | DocType |
lyapunov functional.,lyapunov functional,neural network,dissipativity,lyapunov function,linear matrix inequality | Matrix (mathematics),Control theory,Control engineering,Parametric statistics,Artificial neural network,Lyapunov functional,Lyapunov functionals,Mathematics | Journal |
Volume | Issue | ISSN |
5 | 3 | 17518520 |
Citations | PageRank | References |
10 | 0.66 | 10 |
Authors | ||
4 | ||