Name
Abstract | ||
|---|---|---|
In several relevant applications to the solution of signal processing tasks in real time, a cellular neural network (CNN) is required to be convergent, that is, each solution should tend toward some equilibrium point. The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's invariance principle, for studying convergence and stability of the differential inclusions modeling the dynamics of the full-range (FR) model of CNNs. The applicability of the method is demonstrated by obtaining a rigorous proof of convergence for symmetric FR-CNNs. The proof, which is a direct consequence of the fact that a symmetric FR-CNN admits a strict Lyapunov function, is much more simple than the corresponding proof of convergence for symmetric standard CNNs. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1155/2009/730968 | EURASIP Journal on Advances in Signal Processing |
Keywords | Field | DocType |
equilibrium point,symmetric fr-cnn,extended lasalle,symmetric fr-cnns,cellular neural network,differential inclusion,rigorous proof,direct consequence,symmetric standard cnns,full-range cellular neural network,invariance principle,generalized version,corresponding proof,stability analysis,symmetric matrices,very large scale integration,differential equations,hypercubes,invariance,convergence,stability,cellular neural networks,mathematical model,lyapunov function,set theory | Differential inclusion,Convergence (routing),Lyapunov function,Applied mathematics,Invariance principle,Invariant (physics),Computer science,Symmetric matrix,Artificial intelligence,Cellular neural network,Machine learning,LaSalle's invariance principle | Journal |
Volume | Issue | ISSN |
2009, | 1 | 1687-6180 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauro Di Marco | 1 | 205 | 18.38 |
| Mauro Forti | 2 | 398 | 36.80 |
| Massimo Grazzini | 3 | 131 | 11.01 |
| Luca Pancioni | 4 | 207 | 17.58 |