System identification by cellular neural networks (CNN): Linear interpolation of nonlinear weight functions
Research output: Contribution to journal › Conference article › Contributed › peer-review
Contributors
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
Original language | English |
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Article number | 41 |
Pages (from-to) | 353-358 |
Number of pages | 6 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5839 |
Publication status | Published - 2005 |
Peer-reviewed | Yes |
Conference
Title | Bioengineered and Bioinspired Systems II |
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Duration | 9 - 11 May 2005 |
City | Seville |
Country | Spain |
External IDs
ORCID | /0000-0001-7436-0103/work/173513948 |
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Keywords
ASJC Scopus subject areas
Keywords
- CNN, Identification, Linear interpolation technique, Optimisation