Neural Approximation of Empirical Functions

• Authors: J. Roj
• Languages of publication: EN

Abstracts

EN

The paper presents the results of simulation studies of selected neural network structures used for non-linear function approximation based on a limited accuracy data. There was performed the analysis of the interdependence of the network structure and the size of the set of learning patterns. The approximation inaccuracy was expressed by the uncertainty interval width. The approximation properties of the neural method were compared with those of the piece-wise linear and polynomial: "cubic" and "spline" methods.

Keywords

EN

84.35.+i; 07.05.Mh; 06.20.-f; 06.20.F-; 06.20.fb; 02.60.Ed

Discipline

• 06.20.-f: Metrology
• 06.20.fb: Standards and calibration
• 84.35.+i: Neural networks(for optical neural networks, see 42.79.Ta; see also 07.05.Mh Neural networks, fuzzy logic, artificial intelligence in computers in experimental physics; 87.18.Sn in biological complexity)
• 06.20.F-: Units and standards
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence
• 02.60.Ed: Interpolation; curve fitting

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2013
• Volume: 124
• Issue: 3
• Pages: 554-557

Dates

published

2013-09

Contributors

author

J. Roj

• Institute of Measurement Science, Electronics and Control at the Silesian University of Technology Akademicka 10, 44-100 Gliwice, Poland

References

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Identifiers

DOI

10.12693/APhysPolA.124.554