A Modification of Training and Recognition Algorithms for Recognition of Abnormal Behavior of Dynamic Systems

Victor Shcherbinin, Valeriy Kostenko

2013

Abstract

We consider the problem of automatic construction of algorithms for recognition of abnormal behavior segments in phase trajectories of dynamic systems. The recognition algorithm is constructed using a set of examples of normal and abnormal behavior of the system. We use axiomatic approach to abnormal behavior recognition to construct abnormal behavior recognizers. In this paper we propose a modification of the genetic recognizer construction algorithm and a novel DTW-based recognition algorithm within this approach. The proposed modification reduces search space for the training algorithm and gives the recognition algorithm more information about phase trajectories. Results of experimental evaluation show that the proposed modification allows to reduce the number of recognition errors by an order of magnitude and to reduce the training time by a factor of 2 in comparison to the existing recognizer and recognizer construction algorithm.

References

  1. Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21-27.
  2. Hamming, R. W. (1950). Error detecting and error correcting codes. Bell System Tech. J., 29:147-160.
  3. Hassani, H. (2007). Singular spectrum analysis: Methodology and comparison. Journal of Data Science, 5(2):239-257.
  4. Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition.
  5. Keogh, E. J. and Pazzani, M. J. (2001). Derivative dynamic time warping. In First SIAM International Conference on Data Mining (SDM2001).
  6. Kostenko, V. A. and Shcherbinin, V. V. (2013). Training methods and algorithms for recognition of nonlinearly distorted phase trajectories of dynamic systems. Optical Memory and Neural Networks, 22:8-20.
  7. Kovalenko, D. S., Kostenko, V. A., and Vasin, E. A. (2005). Investigation of applicability of algebraic approach to analysis of time series. In Proceedings of II International Conference on Methods and Tools for Information Processing, pages 553-559. (in Russian).
  8. Kovalenko, D. S., Kostenko, V. A., and Vasin, E. A. (2010). A genetic algorithm for construction of recognizers of anomalies in behaviour of dynamical systems. In Proceedings of 5th IEEE Int. Conf. on Bio Inspired Computing: Theories and Applications, pages 258- 263. IEEEPress.
  9. Mü ller, M. (2007). Information Retrieval for Music and Motion. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
  10. Rudakov, K. V. and Chekhovich, Y. V. (2003). Algebraic approach to the problem of synthesis of trainable algorithms for trend revealing. Doklady Mathematics, 67(1):127-130.
  11. Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
  12. Vapnik, V. (1998). Statistical Learning Theory. Interscience.
  13. Vorontsov, K. V. (2004). Combinatorial substantiation of learning algorithms. Journal of Comp. Maths Math. Phys, 44(11):1997-2009.
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Paper Citation


in Harvard Style

Shcherbinin V. and Kostenko V. (2013). A Modification of Training and Recognition Algorithms for Recognition of Abnormal Behavior of Dynamic Systems . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 103-110. DOI: 10.5220/0004552201030110


in Bibtex Style

@conference{ecta13,
author={Victor Shcherbinin and Valeriy Kostenko},
title={A Modification of Training and Recognition Algorithms for Recognition of Abnormal Behavior of Dynamic Systems},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004552201030110},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - A Modification of Training and Recognition Algorithms for Recognition of Abnormal Behavior of Dynamic Systems
SN - 978-989-8565-77-8
AU - Shcherbinin V.
AU - Kostenko V.
PY - 2013
SP - 103
EP - 110
DO - 10.5220/0004552201030110