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Tytuł artykułu

Person movement prediction using artificial neural networks with dynamic training on a fixed-size training data set

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Significant technical development over the last years has lately been showing more and more promise of making the vision of smart environments come true. The role of future smart environments lies in proactive interaction. Prediction of user’s actions plays a vital role in such interaction. This paper presents a method based on artificial neural networks designed to accommodate the problem of person movement prediction. The paper explores the importance of dynamic training in prediction of nonstationary time series. An approach to dynamic training, based on the so-called on-the-fly training, is presented.
Rocznik
Strony
33--46
Opis fizyczny
Bibliogr. 23 poz., fig., tab.
Twórcy
  • Department of Control and Information Systems, Faculty of Electrical Engineering, University of Ţilina, Univerzitná 1, 010 26 Ţilina, Slovak Republic
autor
  • Department of Control and Information Systems, Faculty of Electrical Engineering, University of Ţilina, Univerzitná 1, 010 26 Ţilina, Slovak Republic
Bibliografia
  • [1] WEISER, M.: The Computer for the 21st Century. In: Scientific American: vol. 265, no. 3. 1991
  • [2] COOK, D., DAS, S.: Smart Environments: Technology, Protocols and Applications. Wiley-Interscience. Hoboken, New Jersey, 2005. ISBN 0-471-54448-5
  • [3] ROY, A., DAS, S.K., BASU, K.: A Predictive Framework for Location-Aware Resource Management in Smart Homes. In: IEEE Transactions on Mobile Computing, Vol. 6. 2007. ISSN: 1536-1233
  • [4] BHATTACHARYA, A., DAS, S.K.: LeZi-update: An information-theoretic framework for personal mobility tracking in PCS networks. In: Wireless Networks, vol. 8, no. 2. Kluwer Academic Publishers, 2002
  • [5] MOZER, M.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: Proceedings of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments. Menlo Park, CA: AAAI Press. 1998
  • [6] VINTAN, L., GELLERT, A., PETZOLD, J., UNGERER, T.: Person Movement Prediction Using Neural Networks. 2004. Universität Augsburg, Fakultät für Angewandte Informatik: Technical Report
  • [7] PRIYANTHA, N.B.: The Cricket Indoor location system. Massachusetts Institute of Technology, Dissertation Thesis. 2005
  • [8] KRUMM., J., ET AL.: Multi-camera multi-person tracking for Easy Living. In: Proceedings of the Third IEEE International Workshop on Visual Surveillance. 2000. ISBN: 0-7695-0698-4
  • [9] ORR, R.J., ABOWD, G.D.: The Smart Floor: A Mechanism for Natural User Identification and Tracking. In: CHI'00 extended abstracts on Human factors in computing systems. Georgia Institute of Technology. 2000
  • [10] TAPIA, E., INTILLE, S., LARSON, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Pervasive Computing, Second International Conference, PERVASIVE. 2004
  • [11] PARK S., KAUTZ H.: Hierarchical Recognition of Activities of Daily Living using Multi-Scale, Multi-Perspective Vision and RFID. In: Intelligent Environments, IET 4th International Conference. 2008. ISBN 978-0-86341-894-5
  • [12] PETZOLD, J., ET AL.: Global State Context Prediction Techniques Applied to a Smart Office Building. In: Communication Networks and Distributed Systems Modeling and Simulation Conference. San Diego, California. 2004
  • [13] ROY, N.: A Context-Aware Learning, Prediction and Mediation Framework for Resource Management in Smart Pervasive Environments. University of Texas at Arlington, Dissertation Thesis. 2008
  • [14] VAN DER SMAGT, P., KRÖSE, B.: An Introduction to Neural Networks. 1996. http://www.avaye.com/files/articles/nnintro/nn_intro.pdf
  • [15] WILSON, D.R., MARTINEZ, T.R.: The General Inefficiency of Batch Training for Gradient Descent Learning. In: Neural Networks. Elsevier, 2003
  • [16] RIEDMILLER, M., BRAUN, H.: A Direct Adaptive Method for Faster Backpropagation Learning: the Rprop Algorithm. In: IEEE International Conference on Neural Networks. 1993
  • [17] IGEL, C., HÜSKEN, M.: Improving the Rprop Learning Algorithm. In: Proceedings of the Second International Symposium on Neural Computation, NC’2000. 2000
  • [18] YAO, X.: Evolving Artificial Neural Networks. In: Proceedings of the IEEE, vol. 87: 9. 1999. ISSN 0018-9219 http://www.gpa.etsmtl.ca/cours/sys843/pdf/Ref2.pdf
  • [19] HSIEH, T.J., HSIAO, H.F., YEH, W.C.: Forecasting Stock Markets Using Wavelet Transforms and Recurrent Neural Networks: An Integrated System Based on Artificial Bee Colony Algorithm. In: Applied Soft Computing: Vol. 11, Issue 2. 2010
  • [20] RAMIREZ-ROSADO, I.J., FERNANDEZ-JIMENEZ , L.A.: Short-term wind power forecasting using simple recurrent genetically optimized neural networks. In: MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control. 2008
  • [21] WONG, W.K., XIA, M., CHU, W.C.: Adaptive neural network model for time-series forecasting. In: European Journal of Operational Research 207. Elsevier, 2010
  • [22] PLUMMER, E.A.: Time Series Forecasting with Feed-forward Neural Networks: Guidelines an Limitations. Department of Computer Scienceand The Graduate School of The University of Wyoming, MSc Thesis. 2000
  • [23] POMERLEAU, D.A.: Efficient Training of Artificial Neural Networks for Autonomous Navigation. In: Neural Computation: 3. 1991
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-438894b9-4044-4d4f-b07b-5b4756ab1f6b
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