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 |
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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 |
Konferenz
Titel | Bioengineered and Bioinspired Systems II |
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Dauer | 9 - 11 Mai 2005 |
Stadt | Seville |
Land | Spanien |
Externe IDs
ORCID | /0000-0001-7436-0103/work/173513948 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- CNN, Identification, Linear interpolation technique, Optimisation