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Authors: Philippe Thomas ; William Derigent and Marie-Christine Suhner

Affiliation: Université de Lorraine, France

ISBN: 978-989-758-054-3

Keyword(s): indoor air quality, neural networks, relearning, control charts

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Indoor air quality is a major determinant of personal exposure to pollutants in today’s world since people spend much of their time in numerous different indoor environments. The Anaximen company develops a smart and connected object named Alima, which can measure every minute several physical parameters: temperature, humidity, concentrations of COV, CO2, formaldehyde and particulate matter (pm). Beyond the measurement aspect, Alima presents some data analysis feature named ‘predictive analytics’, whose primary aim is to predict the evolution of indoor pollutants in time. In this article, the neural network (NN) model,embedded in this object and designed for pollutant prediction, is presented. In addition with this NN model, this article also details an approach where batch learning is performed periodically when a too important drift between the model and the system is detected. This approach is based on control charts.

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Paper citation in several formats:
Thomas, P.; Derigent, W. and Suhner, M. (2014). Prediction Model Adaptation Thanks to Control Chart Monitoring - Application to Pollutants Prediction.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 172-179. DOI: 10.5220/0005075501720179

@conference{ncta14,
author={Philippe Thomas. and William Derigent. and Marie{-}Christine Suhner.},
title={Prediction Model Adaptation Thanks to Control Chart Monitoring - Application to Pollutants Prediction},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005075501720179},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Prediction Model Adaptation Thanks to Control Chart Monitoring - Application to Pollutants Prediction
SN - 978-989-758-054-3
AU - Thomas, P.
AU - Derigent, W.
AU - Suhner, M.
PY - 2014
SP - 172
EP - 179
DO - 10.5220/0005075501720179

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