Parameters Estimation of a Lotka-Volterra Model in an Application for Market Graphics Processing Units
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Proceedings of the 17th Conference on Computer Science and Intelligence Systems. Hrsg. / Maria Ganzha; Leszek Maciaszek; Marcin Paprzycki; Dominik Slezak. Warsaw: Polish Information Processing Society, 2022. S. 935-938 (Annals of Computer Science and Information System; Band 30).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Parameters Estimation of a Lotka-Volterra Model in an Application for Market Graphics Processing Units
AU - Normatov, Dzhakhongir
AU - Mercorelli, Paolo
N1 - Conference code: 17
PY - 2022/9/26
Y1 - 2022/9/26
N2 - In this paper, a least squares method is used to estimate parameter values in the Lotka-Volterra model. The data used are graphics processing units (GPU) shipment worldwide by three key competitors, namely Nvidia, Intel, AMD. The goal is to quantify the parameter values of a model with minimal error in order to qualitatively solve the problem and fit the raw data as closely as possible. Based on the real measurements, the predator between the competitors is recognized through the identification procedure comparing the sign of the coefficients with the original Lotka-Volterra model structure.
AB - In this paper, a least squares method is used to estimate parameter values in the Lotka-Volterra model. The data used are graphics processing units (GPU) shipment worldwide by three key competitors, namely Nvidia, Intel, AMD. The goal is to quantify the parameter values of a model with minimal error in order to qualitatively solve the problem and fit the raw data as closely as possible. Based on the real measurements, the predator between the competitors is recognized through the identification procedure comparing the sign of the coefficients with the original Lotka-Volterra model structure.
KW - Engineering
KW - Computer Science
KW - Parameter estimation
KW - computational modeling
KW - Graphics processing units
KW - Estimation
KW - Data models
KW - Least mean squares methods
UR - http://www.scopus.com/inward/record.url?scp=85141144048&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/xpl/conhome/9908518/proceeding
UR - https://ieeexplore.ieee.org/document/9909348
UR - https://www.mendeley.com/catalogue/2b5f25a1-ebf7-332f-82b5-46b9f707ee95/
U2 - 10.15439/2022F61
DO - 10.15439/2022F61
M3 - Article in conference proceedings
AN - SCOPUS:85141144048
SN - 9788396589712
T3 - Annals of Computer Science and Information System
SP - 935
EP - 938
BT - Proceedings of the 17th Conference on Computer Science and Intelligence Systems
A2 - Ganzha, Maria
A2 - Maciaszek, Leszek
A2 - Paprzycki, Marcin
A2 - Slezak, Dominik
PB - Polish Information Processing Society
CY - Warsaw
T2 - Conference - 17th Conference on Computer Science and Intelligence Systems
Y2 - 4 September 2022 through 7 September 2022
ER -