Robust sequential learning algorithm for function approximation base on strong tracking filter

Huaiqi Kang*, Caicheng Shi, Peikun He, Baojun Zhao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper addresses the problem that network whose parameters are updated using EKF can not obtain robust performance if the system state saltates when EKF reach stable state. Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the network parameters to obtain robust performance. The winner neuron updating strategy is also employed to reduce the computation load for online application. Experimental results show the proposed algorithm can achieve smaller approximation error and more compact network structure than several other typical sequential learning algorithms.

Original languageEnglish
Title of host publication8th International Conference on Signal Processing, ICSP 2006
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)0780397371, 9780780397378
DOIs
Publication statusPublished - 2006
Event8th International Conference on Signal Processing, ICSP 2006 - Guilin, China
Duration: 16 Nov 200620 Nov 2006

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP
Volume3

Conference

Conference8th International Conference on Signal Processing, ICSP 2006
Country/TerritoryChina
CityGuilin
Period16/11/0620/11/06

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