The Artificial Neural Network Structure Selection Algorithm in the Direct Task of Spectral Reflection Prediction

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Abstract: Novel methods of digital image processing demand various approaches to spectral reflection prediction. The continuing complexity of the models leads to high requirements for computing power; however, this does not always contribute to the convenience and accuracy of forecasts. We offer an easy-to-use method for solving the direct problem of spectral reflection prediction using artificial neural networks. For color practitioners, prediction accuracy in terms of color difference is highly important. For researchers of artificial intelligence, the organization of the network learning process is of overriding interest. Those and other interoperates the necessary and sufficient minimum of the training sample to ensure satisfactory forecast accuracy. In this paper, we determine the size of such a sample. When training a network, we use spectral density instead of spectra. This provides a simplified simulation and improved accuracy of the forecast, which is confirmed experimentally.

WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 14, 2019, Art. #9

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Oleg Milder, Dmitry Tarasov, Andrey Tyagunov, "The Artificial Neural Network Structure Selection Algorithm in the Direct Task of Spectral Reflection Prediction," WSEAS Transactions on Systems and Control, vol. 14, pp. 65-70, 2019, DOI:
Oleg Milder, Dmitry Tarasov, Andrey Tyagunov. The Artificial Neural Network Structure Selection Algorithm in the Direct Task of Spectral Reflection Prediction. WSEAS Transactions on Systems and Control. 2019;14:65-70.
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