Adaptive Mechanisms for Parallelization of the Genetic Method of Neural Network Synthesis

Leoshchenko, Serhii; Oliinyk, Аndrii; Subbotin, Sergey

doi:10.1109/acit49673.2020.9208905

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The work of the solution begins with generating initial groups of input signals using correlation; the system then updates the models and groups using a genetic algorithm [17][18][19]. In parallel, genetic operators optimize individual models in each subgroup by making changes to the topology of neural models (for example, model length and layer parameters) [17][18][19]. The end result of these actions is an ensemble model optimized for anomaly detection.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Leoshchenko,

Oliinyk,

Subbotin

et al. 2023

Computer Modeling and Intelligent Systems

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This paper is devoted to the problem of detecting and classifying anomalies for time series data. Some of the important applications of time series anomaly detection are healthcare, fraud detection, and system failure recognition. Despite scientists extensive experience in detecting anomalies in time series, most methods look for individual objects that differ from ordinary objects, but do not take into account the specifics of the data sequence [1]. In this paper, a method for detecting anomalies and clastering time series based on neuroevolutionary approaches is proposed. Prediction-based methods are used to detect anomalies: statistical and deep neural networks are used. Classical clustering methods that accept statistical parameters of series were used for clustering.

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Section: Proposed Methodsmentioning

confidence: 99%

“…crossing, mutating, and selecting the best models for individual clusters; 3. syncing solutions; 4. multi-parent crossover[17][18][19]; 5. evaluation of the ensemble solution.…”

mentioning

confidence: 99%

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Leoshchenko,

Oliinyk,

Subbotin

et al. 2023

Computer Modeling and Intelligent Systems

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Mechanisms of Fine Tuning of Neuroevolutionary Synthesis of Artificial Neural Networks

Leoshcheko

Oliinyk

Субботин

et al. 2021

2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)

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The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

Leoshchenko¹,

Oliinyk²,

Субботин³

et al. 2021

RIC

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Context. The problem of structural modification of pre-synthesized models based on artificial neural networks to ensure the property of interpretation when working with big data is considered. The object of the study is the process of structural modification of artificial neural networks using adaptive mechanisms. Objective of the work is to develop a method for structural modification of neural networks to increase their speed and reduce resource consumption when processing big data. Method. A method of structural adjustment of neural networks based on adaptive mechanisms borrowed from neuroevolutionary synthesis methods is proposed. At the beginning, the method uses a system of indicators to evaluate the existing structure of an artificial neural network. The assessment is based on the structural features of neuromodels. Then the obtained indicator estimates are compared with the criteria values for choosing the type of structural changes. Variants of mutational changes from the group of methods of neuroevolutionary modification of the topology and weights of the neural network are used as variants of structural change. The method allows to reduce the resource intensity during the operation of neuromodels, by accelerating the processing of big data, which expands the field of practical application of artificial neural networks. Results. The developed method is implemented and investigated by the example of using a recurrent artificial network of the long short-term memory type when solving the classification problem. The use of the developed method allowed speed up of the neuromodel with a test sample by 25.05%, depending on the computing resources used. Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in the structural adjustment of pre-synthesized neuromodels for further solving problems of diagnosis, forecasting, evaluation and pattern recognition using big data. The prospects for further research may consist in a more fine-tuning of the indicator system to determine the connections encoding noisy data in order to further improve the accuracy of models based on neural networks.

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Adaptive Mechanisms for Parallelization of the Genetic Method of Neural Network Synthesis

Cited by 3 publications

References 26 publications

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Mechanisms of Fine Tuning of Neuroevolutionary Synthesis of Artificial Neural Networks

The Method of Structural Adjustment of Neural Network Models to Ensure Interpretation

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