An Extended-Input GRNN and its Application | Zendy

Ivan Izonin | Zendy; Natalia Kryvinska | Zendy; Roman Tkachenko | Zendy; Khrystyna Zub | Zendy; Pavlo Vitynskyi | Zendy

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An Extended-Input GRNN and its Application

Author(s) -

Ivan Izonin,

Natalia Kryvinska,

Roman Tkachenko,

Khrystyna Zub,

Pavlo Vitynskyi

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.11.044

Subject(s) - computer science , decomposition , regression , artificial neural network , gaussian , extension (predicate logic) , scheme (mathematics) , algorithm , data mining , artificial intelligence , statistics , mathematics , ecology , mathematical analysis , physics , quantum mechanics , biology , programming language

A new extended-input General Regression Neural Network scheme is proposed. The main objective of such a step was to increase the accuracy of the regression tasks. Such an extension is based on using of the Ito decomposition. This scheme is more appropriate in comparison with existing ones and provides an increase of the prediction accuracy due to the high approximation properties of this decomposition. The developed ANN is used to solve the missing data recovery task. This real dataset was collected by the IoT device, and it is characterized by a large number of passes. A number of practical experiments were carried out on setting of the optimal parameters of the proposed scheme. It has been established that the values of Gaussian functions deviations greater than 0.1 greatly increase the errors of extended-input GRNN. In addition, the larger than the second order Ito decomposition does not improve the accuracy of the ANN and substantially increases the duration of its use. Experimentally established the highest accuracy of the developed ANN in comparison with existing methods.

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