Prediction Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network

Autor:	Bo Wen Wei, Sheng Jun Peng, Zhen Xiang Jiang, Liang Hui Li
Rok vydání:	2014
Předmět:	Engineering Requirements engineering Artificial neural network Control theory business.industry Convergence (routing) General Medicine Kalman filter Deformation (meteorology) business Safety monitoring
Zdroj:	Applied Mechanics and Materials. :4076-4079
ISSN:	1662-7482
DOI:	10.4028/www.scientific.net/amm.513-517.4076
Popis:	By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::826c72c646b0578f75aaa7fc8ef3b5b1 https://doi.org/10.4028/www.scientific.net/amm.513-517.4076 Zobrazit plný text záznamu