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A Neural Network Model for Currency Arbitrage Detection

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7367)

Abstract

The currency arbitrage detection is to find a proper currency conversion sequence that can make the most currency arbitrage. In this paper, the currency arbitrage detection is described as a energy function. And then a Lotka-Volterra (LV) recurrent neural network (RNN) is proposed to obtain the minimum points of the energy function. Simulations demonstrate that the proposed LV RNN is a practical and effective model for the currency arbitrage detection.

Keywords

  • currency arbitrage detection
  • Energy Function
  • Minimum Points
  • Lotka-Volterra Recurrent Neural Networks
  • Stable Attractors

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References

  1. Soon, W.M., Ye, H.Q.: Currency arbitrage detection using a binary integer programming model. International Journal of Mathematical Education in Science and Technology 42(3), 369–376 (2011)

    CrossRef  MathSciNet  Google Scholar 

  2. Hopfield, J.J., Tank, D.W.: “Neural” computation of decisions in optimization problem. Biol. Cybern. 52(3), 141–152 (1985)

    MathSciNet  MATH  Google Scholar 

  3. Tang, H.J., Tan, K.C., Yi, Z.: A columnar competitive model for solving combinatorial optimization problems. IEEE Trancsaction on Neural Networks 15(6), 1568–1573 (2004)

    CrossRef  Google Scholar 

  4. Qu, H., Yi, Z., Tang, H.J.: Improving local minima of columnar competitive model for TSPs. IEEE Tansactions on Circuits and Systems-I: Regular Papers 53(6), 1353–1362 (2006)

    CrossRef  MathSciNet  Google Scholar 

  5. Teoh, E.J., Tan, K.C., Tang, H.J., Xiang, C., Goh, C.K.: An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem. Neurocomputing 71, 1359–1372 (2008)

    CrossRef  Google Scholar 

  6. Budinich, M.: A self-organising neural network for the travelling salesman problem that is competitive with simulated annealing. Neural Computation 8(2), 416–424 (1996)

    CrossRef  Google Scholar 

  7. Yi, Z.: Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks. IEEE Transactions on Neural Networks 21(3), 494–507 (2010)

    CrossRef  Google Scholar 

  8. Fukai, T., Tanaka, S.: A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winner-share-all. Neural Computation 9(1), 77–97 (1997)

    CrossRef  MATH  Google Scholar 

  9. Asai, T., Fukai, T., Tanaka, S.: A subthreshold MOS circuit for the Lotka-Volterra neural network porducing the winner-take-all solutions. Neural Networks Letter 12, 211–216 (1999)

    CrossRef  Google Scholar 

  10. Asai, T., Ohtani, M., Yonezu, H.: Analog integrated circuits for the Lotka-Volterra competitive neural networks. IEEE Trans. Neural Networks 10(5), 1222–1231 (1999)

    CrossRef  Google Scholar 

  11. Hahnloser, R.H., Seung, H.S., Slotine, J.J.: Permitted and forbidden sets in symmetric threshold-linear networks. Neural Computation 15(3), 621–638 (2003)

    CrossRef  MATH  Google Scholar 

  12. Yi, Z., Tan, K.K.: Convergence analysis of recurrent neural networks. Kluwer Academic Publishers (2004)

    Google Scholar 

  13. Yi, Z., Tan, K.K.: Dynamical stability conditions for Lotka-Volterra recurrent neural networks with delays. Physical Review E 66, 011910 (2002)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhang, Z. (2012). A Neural Network Model for Currency Arbitrage Detection. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-31346-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31345-5

  • Online ISBN: 978-3-642-31346-2

  • eBook Packages: Computer ScienceComputer Science (R0)