Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID 627372 12 pages doi 2010 627372 Research Article A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks Paul Honeine EURASIP Member 1 Cedric Richard 2 Jose Carlos M. Bermudez 3 Jie Chen 2 and Hichem Snoussi1 1 Institut Charles Delaunay Universite de Technologie de Troyes 6279 UMR CNRS 12 rue Marie Curie BP2060 10010 Troyes Cedex France 2Fizeau Laboratory Observatoire de la Côte d Azur Universite de Nice Sophia-Antipolis 6525 UMR CNRS 06108 Nice France 3 Department of Electrical Engineering Federal University of Santa Catarina 88040-900 Florianopolis SC Brazil Correspondence should be addressed to Paul Honeine Received 30 October 2009 Revised 8 April 2010 Accepted 9 May 2010 Academic Editor Xinbing Wang Copyright 2010 Paul Honeine et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Wireless sensor networks rely on sensor devices deployed in an environment to support sensing and monitoring including temperature humidity motion and acoustic. Here we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning. These methods are however not suitable for distributed learning in sensor networks as the order of models scales linearly with the number of deployed sensors and measurements. In order to circumvent this drawback we propose to design reduced order models by using an easy to compute sparsification criterion. We also propose a kernel-based least-mean-square algorithm for updating the model parameters using data collected by each sensor. The relevance of our approach is illustrated by two applications that consist
