Hybrid Neuron Networks Based on Q- , W- And Classical Neurons

Chumachenko, О. І.; Dychko, S. T.; Rizhiy, А. R.

doi:10.18372/1990-5548.62.14385

Cited by 2 publications

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“…This article was considered with the problem of structural-parametric synthesis of a hybrid neural network based on the use of multilayer perceptron. Hybridization is achieved through the use of artificial neurons of different types, namely Q-neuron, W-neuron, and classic perceptron [2].…”

Section: Overview оF Methodsmentioning

confidence: 99%

Hybrid Neuron Networks Based on Radial Basis Network With Different Radial Basis Function

Chumachenko

Dychko

Rizhiy

2020

ECS

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It is considered the problem of structural-parametric synthesis of a hybrid neural networks based on the use of radial basis network. Hybridization is achieved through the use of various radial basis functions: Gaussian, multivariate, inverse quadratic, inverse multivariate, thin plate spline, linear, cubic, wavelet functions. The problem of structural-parametric synthesis of hybrid neural network consists in the optimal choice of the number of layers, the number of neurons in the layers, the order of alternation of layers with different neurons. The problem of optimal choice of the number of network cascades and the type of radial basis function in each cascade of the number of layers is solved. Examples of a hybrid neural network synthesis using this methodology for classification and prediction tasks solution are given.IndexTerms-Hybrid neural network; structural-parametric synthesis; optimization problem.

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Section: Overview оF Methodsmentioning

confidence: 99%

Hybrid Neuron Networks Based on Radial Basis Network With Different Radial Basis Function

Chumachenko

Dychko

Rizhiy

2020

ECS

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“…The problem of structural-parametric synthesis of HNN is posed, which consists in optimal choice of basic neural network topology, number of layers, number of neurons in the layers, order of layers alternation with neurons of different types [3].…”

Section: Problem Statementmentioning

confidence: 99%

“…obtaining new useful properties. Structural-parametric synthesis of HNN in this case includes not only the selection of the number of network layers and the number of neurons, but also the sequence of network architectures or combinations of neurons of different types within one architecture [3].…”

Section: Introductionmentioning

confidence: 99%

Algorithm of Hybrid GMDH-Network Construction for Time Series Forecast

Chumachenko

Kot

Mandrenko

2020

ECS

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It is considered the problem of structural-parametric synthesis of a hybrid neural networks based on the use of Group Method of Data Handling neural network. Hybridization is achieved through the use of various neurons: classical, nonlinearAdaline, R-neuron, W-neuron, Wavelet-neuron. The problem of structural-parametric synthesis of hybrid neural network consists in the optimal choice of the number of layers, the number of neurons in the layers, the order of alternation of layers with different neurons. As an example it is considered the forecast problem solution with help of hybrid neural networks based on the data of the COVID-19 pandemic, collected by Johns Hopkins University. A MAPE criterion was used for quality assessment.IndexTerms-Hybrid neural network; structural-parametric synthesis; forecast of time series.Направление научной деятельности: системный анализ, искусственные нейронные сети. Количество публикаций: более 80 научных работ.

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Hybrid Neuron Networks Based on Q- , W- And Classical Neurons

Cited by 2 publications

References 0 publications

Hybrid Neuron Networks Based on Radial Basis Network With Different Radial Basis Function

Hybrid Neuron Networks Based on Radial Basis Network With Different Radial Basis Function

Algorithm of Hybrid GMDH-Network Construction for Time Series Forecast

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