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Self-adaptation algorithm for neural networks with search behavior

  • Elementary Particle Physics and Field Theory
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Russian Physics Journal Aims and scope

Abstract

In the present paper, a training algorithm with nontraditional capabilities and self-adaptation is suggested for a new generation of neural networks with search behavior. The results obtained open up new opportunities for progress in the physics of living systems in the direction of modeling of purposeful search behavior at the insect level. They enable systems of adaptive control, considering changes in environmental conditions and in properties of the controllable object in the control process, to be developed. The algorithm allows both traditional supervisory training of a neural network with a priori known answers and finish learning during its functioning in accordance with preassigned criteria when knowledge of required states of neuron network outputs is lacking.

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Authors
  1. T. F. Baskanova
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  2. Yu. P. Lankin
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Additional information

Krasnoyarsk State University; Institute of Biophysics of the Siberian Branch of the Russian Academy of Sciences. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 6, pp. 47–51, June, 2000.

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Baskanova, T.F., Lankin, Y.P. Self-adaptation algorithm for neural networks with search behavior. Russ Phys J 43, 483–487 (2000). https://doi.org/10.1007/BF02508628

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  • DOI: https://doi.org/10.1007/BF02508628

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