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Linear-wavelet models applied to the identification of a two-link manipulator

Research output: Chapter in Book/Report/Conference proceedingConference contribution

This paper shows that a wavelet network and a linear term can be advantageously combined for the purpose of non linear system identification. The theoretical foundation of this approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such nonlinear regression structures, termed linear-wavelet models, is described. For illustration, sim ulation data are used to identify a model for a two-link robotic manipulator. The results show that the introduction of wavelets does improve the prediction ability of a linear model.
Original languageEnglish
Title of host publication21st IASTED international conference on modelling, identification and control (MIC 2002)
PublisherACTA Press
Pages479-484
Number of pages6
ISBN (Print)978-0889863194
Publication statusPublished - 2002

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