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Synchronization of Multivariate Captors with an Autoadaptive Neural Method

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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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Authors and Affiliations

  1. Centre de Mathématiques et leurs Applications (URA 1611), Ecole Normale Supérieure de Cachan, 61, avenue du Président Wilson –, 94235, Cachan Cedex, France

    Jérôme Lacaille

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  1. Jérôme Lacaille
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Lacaille, J. Synchronization of Multivariate Captors with an Autoadaptive Neural Method. Journal of Intelligent and Robotic Systems 21, 155–165 (1998). https://doi.org/10.1023/A:1007937606644

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  • DOI: https://doi.org/10.1023/A:1007937606644

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