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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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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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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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