An algorithmic approach to adaptive state filtering using recurrent neural networks

• Published in: IEEE transactions on neural networks Vol. 12; no. 6; pp. 1411 - 1432
• Main Authors: Parlos, A.G., Menon, S.K., Atiya, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2001
• Subjects: Adaptive filters; Condition monitoring; Fault detection; Fault diagnosis; Filtering algorithms; Nonlinear equations; Parameter estimation; Process control; Recurrent neural networks; State estimation
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/72.963777

Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.