Adaptive Bivariate Function Generation based on Chebyshev-Polynomials
| Autor: | Steffen Paul, Pascal Seidel, Jochen Rust |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Chebyshev polynomials
Computer science 05 social sciences Function generator Bivariate function 02 engineering and technology Bivariate analysis Extension (predicate logic) Adaptive hardware 020202 computer hardware & architecture Set (abstract data type) Cover (topology) ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION 0502 economics and business 0202 electrical engineering electronic engineering information engineering Algorithm 050203 business & management |
| Zdroj: | NEWCAS |
| DOI: | 10.1109/newcas44328.2019.8961270 |
| Popis: | In this paper an adaptive hardware architecture for high-performance bivariate numerical function approximation is presented. Orthogonal Chebyshev-Polynomials are exploited that cover incremental accuracy refinements. Additionally, switching between two numeric functions is easily deployable by changing the set of polynomial coefficients. For evaluation, different configurations of the proposed hardware function generator are implemented and analyzed considering three bivariate numeric functions. The resulting performance highlights this approach to be a powerful extension for bivariate function approximation. |
| Databáze: | OpenAIRE |
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