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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 6, NOVEMBER 2001 1411 An Algorithmic Approach to Adaptive State Filtering
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 6, NOVEMBER 2001 1411
An Algorithmic Approach to Adaptive State Filtering
Using Recurrent Neural Networks
Alexander G. Parlos, Senior Member, IEEE, Sunil K. Menon, Member, IEEE, and Amir F. Atiya, Senior Member, IEEE
Abstract--On-line estimation of variables that are difficult
or expensive to measure using known dynamic models has
been a widely studied problem. Applications of this problem
include time-series forecasting, process control, parameter and
state estimation, and fault diagnosis. In this paper, 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. However, unlike
the Kalman filter and its extensions, the proposed algorithms
make minimal assumptions regarding the underlying nonlinear
dynamics and their noise statistics. Nonadaptive and adaptive
state filtering algorithms are presented with both off-line and
on-line learning stages. The proposed algorithms are implemented
using feedforward and recurrent neural network and comparisons

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology
Parlos, Alexander - Department of Mechanical Engineering, Texas A&M University

 

Collections: Computer Technologies and Information Sciences; Engineering