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Application of cluster analysis and autoregressive neural networks for the noise diagnostics of the IBR-2M reactor

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Abstract

The pattern recognition methodologies and artificial neural networks were used widely for the IBR-2M pulsed reactor noise diagnostics. The cluster analysis allows a detailed study of the structure and fast reactivity effects of IBR-2M and nonlinear autoregressive neural network (NAR) with local feedback connection allows predicting slow reactivity effects. In this work we present results of a study on pulse energy noise dynamics and prediction of liquid sodium flow rate through the core of the IBR-2M reactor using cluster analysis and an artificial neural network.

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References

  1. Ch. C. Aggarwal and Ch. K. Reddy, Data Clustering: Theory, Algorithms, and Applications (Chapman and Hall, CRC, London, Boca Raton, 2013), pp. 87–106.

  2. V. D. Anan’ev et al., “Physical startup of the upgraded IBR–2 reactor (IBR–2M),” JINR Commun. R13-2012-41 (Dubna, 2012).

  3. V. D. Anan’ev et al., “Power startup of the upgraded IBR–2 reactor (IBR–2M),” JINR Commun. R13-2012-42 (Joint Inst. Nucl. Res., Dubna, 2012).

  4. V. D. Anan’ev et al., “Physical startup of the research IBR–2 reactor,” Commun. JINR No. P13-12482 (Joint Inst. Nucl. Res., Dubna, 1979).

  5. L. Ljung, System Identification: Theory for the User, 2nd ed. (Prentice Hall, Englewood Cliffs, NJ, 1998).

  6. C. L. Giles, S. Lawrence, and A. C. Tsoi, “Noisy time series prediction using a recurrent neural network and grammatical inference,” Machine Learning 44, 161–183 (2001).

    Article  MATH  Google Scholar 

  7. K. Anil, J. M. Jain, and K. M. Mohiuddin, “Artificial neural networks: a tutorial,” IEEE Comput. 29 (3), 31–44 (1996).

    Article  Google Scholar 

  8. A. V. Akhterov and A. A. Kiril’chenko, “Bases of the theoretical robotechnics. Artificial neural networks,” KIAM Preprint No. 46 (Keldysh Inst. Math., RAS, Moscow, 2009).

  9. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. (Prentice Hall, Englewood Cliffs, NJ, 1998).

  10. K. Levenberg, “A method for the solution of certain problems in least squares,” Q. Appl. Math. 2, 164–168 (1944).

    MathSciNet  MATH  Google Scholar 

  11. D. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” SIAM J. Appl. Math. 11, 431–441 (1963).

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Yu. N. Pepelyshev.

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Pepelyshev, Y.N., Tsogtsaikhan, T. & Ososkov, G.A. Application of cluster analysis and autoregressive neural networks for the noise diagnostics of the IBR-2M reactor. Phys. Part. Nuclei Lett. 13, 704–707 (2016). https://doi.org/10.1134/S1547477116050381

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  • DOI: https://doi.org/10.1134/S1547477116050381

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