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In this paper, improvement of learning atypical networks (an increase of precision, speed and reliability), used to determine coefficients of functions describing hidden laws acting in a set of empirical data is presented. The learning concerns networks, which are based on rational functions. Proper expression of the used rational function is required. A new element of our approach to the problem...
In this paper, the problem of efficient learning specific neural networks including reciprocal activation functions of the 1/(.) type is discussed. The considered networks can be used, when applying polynomial descriptions, to create symbolic models of unknown laws governing a given set of empirical data. Coefficients of the polynomials are determined in the process of learning the network. However,...
The problem of utilizing atypical neural networks to create a symbolic description of rules governing a set of empirical data is considered. We propose to use fractional–rational or polynomial functions as a versatile tool to describe the unknown empirical–data rules. Our aim is to transform basic forms of these functions to others, suitable for neural implementations, i.e. by means of special–type...
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