Spline-Orthogonal Extended Neo-Fuzzy Neuron

Bodyanskiy, Yevgeniy; Zaychenko, Yuriy; Kulishova, Nonna; Hamidov, Galib

doi:10.1109/cisp-bmei48845.2019.8965840

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…It is interesting to note that J. Schmidhuber [12] believes that just based on GMDH the first deep learning systems were built. Subsequently, based on GMDH, neural networks and neuro-fuzzy systems were proposed, which demonstrated their accuracy and speed in solving a number of problems [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid system of computational intelligence based on bagging and group method of data handling

Bodyanskiy,

Kuzmenko,

Zaichenko

et al. 2024

SRIT

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The paper considers the problem of short- and middle-term forecasting in the financial sphere. To solve this problem, a hybrid system of computational intelligence based on the group method of data handling (GMDH) and bagging, as well as an algorithm for its training, is proposed. The odd stacks of the hybrid system are formed by ensembles of parallel connected subsystems. ARIMA and the GMDH-neo-fuzzy hybrid network were chosen as such subsystems. The proposed system does not require a large training data set, automatically determines the number of stacks during training, and provides online operation. The experimental investigations were conducted using the proposed hybrid system, as well as separately using ARIMA and GMDH-neo-fuzzy. The accuracy of the predictions obtained is compared, based on which the feasibility of using the proposed hybrid system is substantiated.

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