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Dynamic rectification of data via recurrent neural nets and the extended Kalman filter
Author(s) -
Karjala Thomas W.,
Himmelblau David M.
Publication year - 1996
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690420812
Subject(s) - extended kalman filter , kalman filter , recurrent neural network , computer science , rectification , context (archaeology) , control theory (sociology) , noise (video) , state space , autocorrelation , filter (signal processing) , invariant extended kalman filter , algorithm , artificial intelligence , artificial neural network , mathematics , engineering , statistics , computer vision , paleontology , control (management) , voltage , electrical engineering , image (mathematics) , biology
The presence of autocorrelated measurement errors and/or measurement bias in process measurements poses serious problems in the rectification of data taken from dynamic processes. The proposed procedure to resolve these problems involves the use of recurrent neural networks (RNN) and the extended Kalman filter (EKF). By interpreting RNNs within a nonlinear state‐space context, a state‐augmented EKF can be used to optimally estimate both the states of the RNNs and noise and bias models. RNN models can be identified off‐line and utilized for data rectification within the extended Kalman filter in process environments in which badly autocorrelated measurement errors exist in the data. The same technique is also used to estimate measurement bias present in both process input and output variables. This approach has the advantage that models developed from “first principles” are not required and that rectification can be performed solely on the basis of the contaminated dynamic data.