Title :
Incremental least squares methods and the extended Kalman filter
Author :
Bertsekas, Dimitri P.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
Abstract :
Proposes and analyzes nonlinear least squares methods, which process the data incrementally, one data block at a time. Such methods are well suited for large data sets and real time operation, and have received much attention in the context of neural network training problems. The author focuses on the extended Kalman filter, which may be viewed as an incremental version of the Gauss-Newton method. The author provides a nonstochastic analysis of its convergence properties, and discusses variants aimed at accelerating its convergence
Keywords :
Kalman filters; Newton method; convergence of numerical methods; least squares approximations; Gauss-Newton method; convergence properties; extended Kalman filter; incremental least squares methods; nonlinear least squares methods; nonstochastic analysis; Acceleration; Backpropagation; Context modeling; Convergence; Gaussian processes; Least squares methods; Neural networks; Newton method; Recursive estimation; Stochastic processes;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411166
