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A synergy of the Rosenblatt perceptron and the Jordan recurrence principle

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Abstract

The recurrent perceptron RP-1—a combination of the Rosenblatt perceptron and the Jordan network—is proposed. As a result, the perceptron acquires properties of recurrent neural networks, while the learning remains as fast and efficient as in the conventional Rosenblatt model. The recurrent perceptron is tested on the example of an agent education problem, where the “student” is placed into a model environment.

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References

  1. Rosenblatt, F., Principles of Neurodynamics (Perceptrons and the Theory of Brain Mechanisms), Washington D.C.: Spartan Books, 1962.

    MATH  Google Scholar 

  2. Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning Internal Representations by Error Propagation, Parallel distributed processing: Explorations of the Microstructure of Cognition, Cambridge: MIT Press, 1986, pp. 318–362.

    Google Scholar 

  3. Wasserman, P., Neurocomputing: Theory and Practice, New York: Van Nostrand Reinhold, 1990.

    Google Scholar 

  4. Bongard, M.M., The Recognition Problem, Moscow: Nauka, 1967.

    Google Scholar 

  5. Elman, J.L. Finding Structure in time, Cognitive Science, 1990, vol. 14, pp. 179–211.

    Article  Google Scholar 

  6. Jordan, M.I., Serial Order: a Parallel Distributed Processing Approach. Institute of Cognitive Science, University of California, San Diego, 1986, Report 8604.

    Google Scholar 

  7. Haykin S. Neural Networks: A Comprehensive Foundation, 2nd edition, Upper Saddle River, NJ: Prentice Hall, 1998.

    Google Scholar 

  8. Kussul E., Baiduk T., Kasatkina L., Lukovich V., Rosenblatt Perceptrons for Handwritten Digit Recognition, IEEE 0-7803-7044-9, pp. 1516–2001.

  9. Kussul E., Baiduk T., An Improved Method of Handwritten Digit Recognition Tested on MNIST Database, Image and Vision Computing, 2004, vol. 22, pp. 971–981.

    Article  Google Scholar 

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Authors and Affiliations

  1. Riga Technical University, 1 Kalku Street, Riga, LV-1658, Latvia

    S. S. Yakovlev & A. N. Borisov

Authors
  1. S. S. Yakovlev
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  2. A. N. Borisov
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Corresponding author

Correspondence to S. S. Yakovlev.

Additional information

Original Russian Text © S.S. Yakovlev, A.N. Borisov, 2009, published in Avtomatika i Vychislitel’naya Tekhnika, 2009, No. 1, pp. 46–55.

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Yakovlev, S.S., Borisov, A.N. A synergy of the Rosenblatt perceptron and the Jordan recurrence principle. Aut. Conrol Comp. Sci. 43, 31–39 (2009). https://doi.org/10.3103/S0146411609010052

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  • DOI: https://doi.org/10.3103/S0146411609010052

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