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Synchronization of Multivariate Captors with an Autoadaptive Neural Method

Authors
  • Lacaille, Jérôme1
  • 1 Ecole Normale Supérieure de Cachan, Centre de Mathématiques et leurs Applications (URA 1611), 61, avenue du Président Wilson –, Cachan Cedex, 94235, France , Cachan Cedex
Type
Published Article
Journal
Journal of Intelligent & Robotic Systems
Publisher
Springer-Verlag
Publication Date
Feb 01, 1998
Volume
21
Issue
2
Pages
155–165
Identifiers
DOI: 10.1023/A:1007937606644
Source
Springer Nature
Keywords
License
Yellow

Abstract

Before any multivariate analysis scheme (using typicaly simultaneous measurements) it is useful to readjust in time each measurement so that each one refer to the same element of the model. This problem arises frequently in the modelization of an industrial process. It is sometimes possible to propose a dynamic model after a microscopic study of displacements of matter. However such a study is very complex and cannot be conceived by a machine analysis of the data. In this article we present a practical self-adapting methodology which automates the synchronization of captors. This method use a recurrent neural network for the self auto-adaptivity of the synchronization. This network is model and tune automaticaly by an initialization process based on the local stationary phenomena appearing in measurements.

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