Models Updating for Technical Objects State Forecasting

Autor:	Vladimir N. Klyachkin, Yulia Kuvayskova, Victor Krasheninnikov
Rok vydání:	2018
Předmět:	Structure (mathematical logic) Multivariate statistics Series (mathematics) Computer science business.industry Object (computer science) Machine learning computer.software_genre Data modeling Artificial intelligence State (computer science) Time series Focus (optics) business computer
Zdroj:	2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC).
DOI:	10.1109/rpc.2018.8482222
Popis:	Under consideration are the forecasting models based on time series systems and multivariate classifiers, which focus on forecasting of technical objects state. In many cases, these models get out of date as new data become available. Therefore, these models require both the model parameters and its structure to be adjusted. The article addresses methods of parameters and structure updating for models applied to predict object state.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1666aa636402d7b3171d217a568d4d51 https://doi.org/10.1109/rpc.2018.8482222 Zobrazit plný text záznamu