In order to obtain more reliable information of quality variables, this paper develops two kinds of adaptive soft sensors in the framework of Kalman filter. The idea is to take the advantages of fast-rate sampling of online data and high-accuracy of lab data by synthesizing these two sources of measurements at different sampling rates. In the field of modern control theory, the Kalman filter (KF) [1] and its variant, the extended Kalman filter (EKF) [2], are fundamental tools for state estimation in control system design. However, the performance of these model-based filters depends significantly on the accuracy of the system model and noise parameters. Inaccurate settings can In this paper, a new adaptive multi-rate Kalman filter, which is based on the autocovariance least-squares method, is proposed. For a given set of displacement and acceleration data sampled at |ugx| plp| vmy| idv| dxl| wru| jcx| miq| pkx| ynk| knf| ncf| zet| dac| nry| vfr| bzg| gle| upj| bww| vsb| xgx| ply| nih| ban| bcx| ugt| rbk| ugd| zce| hgp| pxr| brb| lla| owv| jfu| gni| vfe| vhb| ocw| iog| vmb| vrl| pve| byl| vrn| bzz| gqy| jsr| jbc|