Name
Title | ||
|---|---|---|
Finite-Time Stability for Caputo-Katugampola Fractional-Order Time-Delayed Neural Networks. |
Abstract | ||
|---|---|---|
In this paper, an original scheme is presented, in order to study the finite-time stability of the equilibrium point, and to prove its existence and uniqueness, for Caputo–Katugampola fractional-order neural networks, with time delay. The proposed scheme uses a newly introduced fractional derivative concept in the literature, which is the Caputo–Katugampola fractional derivative. The effectiveness of the theoretical results is shown through simulations for two numerical examples. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11063-019-10060-6 | Neural Processing Letters |
Keywords | Field | DocType |
Fractional-order calculus, Neural networks, Finite-time stability, Caputo–Katugampola derivative | Uniqueness,Applied mathematics,Pattern recognition,Equilibrium point,Fractional order calculus,Fractional calculus,Artificial intelligence,Artificial neural network,Mathematics,Finite time | Journal |
Volume | Issue | ISSN |
50 | 1 | 1370-4621 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Assaad Jmal | 1 | 1 | 0.36 |
| abdellatif ben makhlouf | 2 | 5 | 2.27 |
| A. M. Nagy | 3 | 31 | 4.80 |
| Naifar, O. | 4 | 6 | 4.67 |