Research on a self-adaptive prediction model of logistics requirement based on Kalman Filter

2011 ◽  
Author(s):  
Du Qiu ◽  
Su Xisheng ◽  
Xu Zijun
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Adaptive Prediction ◽  
Self Adaptive

scholarly journals Modification of Recursive Kalman Filter Algorithm for Adaptive Prediction of Cyber Resilience for Industrial Systems

2020 ◽  
Vol 23 (3) ◽  
pp. 270-279
Author(s):  
D.S. Lavrova ◽  
D.P. Zegzhda
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Kalman Filter ◽  
Prediction Model ◽  
Early Stage ◽  
Classification Algorithm ◽  
Industrial Systems ◽  
Machine Learning Classification ◽  
Adaptive Prediction ◽  
Kalman Filter Algorithm

This paper describes an approach to modification of the recursive Kalman filter algorithm to obtain adaptive prediction of time series from industrial systems. To ensure cyber resilience of modern industrial systems, it is necessary to detect anomalies in their work at an early stage. For this, data from industrial systems are presented as time series, and an adaptive prediction model combined with machine learning classification algorithm applies to identify anomalies. The effectiveness of the proposed approach is confirmed experimentally.

scholarly journals Adaptive Prediction Model in Prospective Molecular Signature–Based Clinical Studies

2013 ◽  
Vol 20 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Guanghua Xiao ◽  
Shuangge Ma ◽  
John Minna ◽  
Yang Xie
Keyword(s):  
Prediction Model ◽  
Clinical Studies ◽  
Molecular Signature ◽  
Adaptive Prediction

scholarly journals A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215892-215903
Author(s):  
Ji Jin ◽  
Bin Wang ◽  
Min Yu ◽  
Jiang Liu ◽  
Wenbo Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Atmospheric Motion ◽  
Self Adaptive

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

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