IngentaConnect The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rul...

Authors: Moriyama H.¹; Yamashita N.¹; Fukushima M.¹

Source: Computational Optimization and Applications, Volume 26, Number 2, November 2003 , pp. 107-141(35)

Publisher: Springer

Abstract:

In this paper, we consider the Extended Kalman Filter (EKF) for solving nonlinear least squares problems. EKF is an incremental iterative method based on Gauss-Newton method that has nice convergence properties. Although EKF has the global convergence property under some conditions, the convergence rate is only sublinear under the same conditions. One of the reasons why EKF shows slow convergence is the lack of explicit stepsize. In the paper, we propose a stepsize rule for EKF and establish global convergence of the algorithm under the boundedness of the generated sequence and appropriate assumptions on the objective function. A notable feature of the stepsize rule is that the stepsize is kept greater than or equal to 1 at each iteration, and increases at a linear rate of k under an additional condition. Therefore, we can expect that the proposed method converges faster than the original EKF. We report some numerical results, which demonstrate that the proposed method is promising.

Keywords: incremental algorithm; extended Kalman filter; adaptive stepsize; neural network

Language: English

Document Type: Research article

Affiliations: 1: Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$42.00 plus tax

The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rule