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Training of Neural Networks with Search Behaviour

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Abstract

The development of the physics of living systems is caused by the necessity of considering the specific features of their adaptive search behavior. This is one of the main properties of living systems distinguishing adaptable living systems from mechanisms and cybernetic robots. An efficient method for studying the adaptive properties of living systems is the construction and investigation of neural network models of human and animal brains ensuring highly efficient behavior under complex dynamic environmental conditions. In [1] we have already suggested the mathematical formalism for a description of self-adapting neural networks and their training that can be used to model the self-organization processes in open living systems. In the present paper, new neural network algorithms of this class are suggested. New capabilities of these algorithms inaccessible for the conventional supervisory algorithms of neural network training are examined.

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Authors and Affiliations

  1. Institute of Biophysics of the Siberian Branch of the Russian Academy of Sciences, Russia

    T. F. Baskanova & Yu. P. Lankin

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  1. T. F. Baskanova
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  2. Yu. P. Lankin
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Baskanova, T.F., Lankin, Y.P. Training of Neural Networks with Search Behaviour. Russian Physics Journal 45, 389–393 (2002). https://doi.org/10.1023/A:1020535225240

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  • DOI: https://doi.org/10.1023/A:1020535225240

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