Back-reference Analogy within Artificial Neural Networks Implementation

We are to address our past approach to tackling back-reference problem which is known to be NP-hard and, thus, impractical in general, however, the applicable and practical approach is required in order to make the search possible within the defined time frame, on that account we present our ‘in-deep’ concept which can be applied in the implementation of artificial neural networks (ANNs), this approach gives us the delay function which can be successfully used in the hypothetical networks, where the immediate transition between neural nodes is to be made delayed according to the structure and maybe even nature, we will further discuss how it can be vital for the newly presented ANNs

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Publication Details

Published
2026-11-09
DOI
https://doi.org/10.33774/coe-2026-zkqxw
Primary Topic
Neural Networks and Applications
Type
preprint
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preprint

Back-reference Analogy within Artificial Neural Networks Implementation

Mirzakhmet B Syzdykov
1 links
Neural Networks and Applications
preprint

Back-reference Analogy within Artificial Neural Networks Implementation

Mirzakhmet B Syzdykov
preprint en

Abstract

We are to address our past approach to tackling back-reference problem which is known to be NP-hard and, thus, impractical in general, however, the applicable and practical approach is required in order to make the search possible within the defined time frame, on that account we present our ‘in-deep’ concept which can be applied in the implementation of artificial neural networks (ANNs), this approach gives us the delay function which can be successfully used in the hypothetical networks, where the immediate transition between neural nodes is to be made delayed according to the structure and maybe even nature, we will further discuss how it can be vital for the newly presented ANNs

Neural Networks and Applications
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Back-reference Analogy within Artificial Neural Networks Implementation — Mirzakhmet B Syzdykov · (2026) | TGRS Research Map | TGRS