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A Continuous-Time Model of Analogue K-Winners-Take-All Neural Circuit

  • Conference paper
Engineering Applications of Neural Networks (EANN 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 311))

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Abstract

A continuous-time model of analogue K-winners-take-all (KWTA) neural circuit which is capable to extraction the K largest from any finite value N unknown distinct inputs, where 1 ≤ K < N, is presented. The model is described by one state equation with discontinuous right-hand side and output equation. A corresponding functional block diagram of the model is given as N feedforward and one feedback hardlimiting neurons, which is used to determine the dynamic shift of inputs. The model combines such properties as high accuracy and convergence speed, low computational and hardware implementation complexity, and independency on initial conditions. Simulation examples demonstrating the model performance are provided.

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References

  1. Majani, E., Erlanson, R., Abu-Mostafa, Y.: On the K-winners-take-all network. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, vol. 1, pp. 634–642. Morgan Kaufmann Publishers Inc., San Francisco (1989)

    Google Scholar 

  2. Atkins, M.: Sorting by Hopfield nets. In: IEEE Int. Joint Conf. on Neural Networks, pp. 65–68. IEEE Press (1982)

    Google Scholar 

  3. Urahama, K., Nagao, T.: K-winners-take-all circuit with 0(N) complexity. IEEE Trans. on Neural Networks 6, 776–778 (1995)

    Article  Google Scholar 

  4. Kwon, T.M., Zervakis, M.: KWTA networks and their applications. Multidimensional Syst. and Signal Processing 6, 333–346 (1995)

    Article  MATH  Google Scholar 

  5. Binh, L.N., Chong, H.C.: A neural-network contention controller for packet switching networks. IEEE Trans. on Neural Networks 6, 1402–1410 (1995)

    Article  Google Scholar 

  6. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)

    Article  Google Scholar 

  7. Cilingiroglu, U., Dake, T.L.E.: Rank-order filter design with a sampled-analog multiple-winners-take-all core. IEEE Journal on Solid-State Circuits 37, 978–984 (2002)

    Article  Google Scholar 

  8. Erlanson, R., Abu-Mostafa, Y.: Analog neural networks as decoders. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, vol. 1, pp. 585–588. Morgan Kaufmann Publishers Inc., San Francisco (1991)

    Google Scholar 

  9. Fish, A., Akselrod, D., Yadid-Pecht, O.: High precision image centroid computation via an adaptive k-winner-take-all circuit in conjunction with a dynamic element matching algorithm for star tracking applications. Analog Integrated Circuits and Signal Processing 39, 251–266 (2004)

    Article  Google Scholar 

  10. Jain, B.J., Wysotzki, F.: Central clustering of attributed graphs. Machine Learning 56, 169–207 (2004)

    Article  MATH  Google Scholar 

  11. Liu, S., Wang, J.: A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans. on Neural Networks 17(6), 1500–1510 (2006)

    Article  Google Scholar 

  12. DeSouza, G.N., Zak, A.C.: Vision for mobile robot navigation: a survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 237–267 (2002)

    Article  Google Scholar 

  13. Graupe, D.: Principles of Artificial Neural Networks, 2nd edn. World Sci. Publisher, Singapore (2007)

    MATH  Google Scholar 

  14. Cichocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. John Wiley & Sons, Chichester (1993)

    MATH  Google Scholar 

  15. Wang, J.: Analysis and design of a k-winners-take-all model with a single state variable and the Heaviside step activation function. IEEE Trans. on Neural Networks 9, 1496–1506 (2010)

    Article  Google Scholar 

  16. Tymoshchuk, P.V.: A dynamic K-winners take all analog neural circuit. In: IVth IEEE Int. Conf. “Perspective Technologies and Methods in MEMS Design”, pp. 13–18. IEEE Press, L’viv (2008)

    Google Scholar 

  17. Liu, Q., Wang, J.: Two k-winners-take-all networks with discontinuous activation functions. Neural Networks 21, 406–413 (2008)

    Article  MATH  Google Scholar 

  18. Tymoshchuk, P.V.: Stability analysis of continuous-time model of K-Winners-Take-All neural circuit. In: XVI Ukrainian-Polish Conf. “CAD in Machinery Design. Implementation and Educational Problems”, pp. 29–30. IEEE Press, L’viv (2008)

    Google Scholar 

  19. Tymoshchuk, P.V.: A discrete-time dynamic K-winners-take-all neural circuit. Neurocomputing 72, 3191–3202 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. EIT Department, Lodz University, 65, Narutovich Str., Lodz, 90-131, Poland

    Pavlo V. Tymoshchuk

  2. CAD Department, L’viv Polytechnic National University, 12, S. Bandera Str., L’viv, 79013, Ukraine

    Pavlo V. Tymoshchuk

Authors
  1. Pavlo V. Tymoshchuk
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Editor information

Editors and Affiliations

  1. Coventry University, Priory Street,, CV1 5FB, Coventry, UK

    Chrisina Jayne

  2. University of Lincoln, LN6 7TS, Lincoln, UK

    Shigang Yue

  3. University of Thrace, 193 Pandazidou st., 68200 N, Orestiada, Greece

    Lazaros Iliadis

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© 2012 Springer-Verlag Berlin Heidelberg

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Tymoshchuk, P.V. (2012). A Continuous-Time Model of Analogue K-Winners-Take-All Neural Circuit. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-32909-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32908-1

  • Online ISBN: 978-3-642-32909-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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