System identification by cellular neural networks (CNN): Linear interpolation of nonlinear weight functions
Publikation: Beitrag in Fachzeitschrift › Konferenzartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Recently CNN with nonlinear weight functions are used for various problems. Thereby nonlinear weights are represented by polynomials or tabulated functions combined with a cubic spline interpolation. In this paper a linear interpolation technique is considered to allow an accurate approximation of nonlinear weight functions in CNN. In a previous publication the Table Minimising Algorithm (TMA) was introduced and applied to the Korteweg-de Vries-equation (KdV). In this contribution new results obtained by applying the algorithm to additional partial differential equations (PDE) will be given and discussed.
Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 41 |
| Seiten (von - bis) | 353-358 |
| Seitenumfang | 6 |
| Fachzeitschrift | Proceedings of SPIE - The International Society for Optical Engineering |
| Jahrgang | 5839 |
| Publikationsstatus | Veröffentlicht - 2005 |
| Peer-Review-Status | Ja |
| Extern publiziert | Ja |
Konferenz
| Titel | Bioengineered and Bioinspired Systems II |
|---|---|
| Dauer | 9 - 11 Mai 2005 |
| Stadt | Seville |
| Land | Spanien |
Externe IDs
| ORCID | /0000-0001-7436-0103/work/173513948 |
|---|
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- CNN, Identification, Linear interpolation technique, Optimisation