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Performability Modeling of Self-Adaptive Systems Based on Extension Neural Rewriting Stochastic Petri Nets

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dc.contributor.author SCLIFOS, Alexei
dc.contributor.author SCLIFOS, Emilia
dc.contributor.author GUȚULEAC, Emilian
dc.date.accessioned 2022-12-27T17:40:01Z
dc.date.available 2022-12-27T17:40:01Z
dc.date.issued 2022
dc.identifier.citation SCLIFOS, Alexei, SCLIFOS, Emilia, GUȚULEAC, Emilian. Performability Modeling of Self-Adaptive Systems Based on Extension Neural Rewriting Stochastic Petri Nets. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 162-167. en_US
dc.identifier.uri https://doi.org/10.52326/ic-ecco.2022/CS.03
dc.identifier.uri http://repository.utm.md/handle/5014/21849
dc.description.abstract Traditional mathematical formalisms are unable to model modern self-adaptive discrete event systems (ADES) because they cannot handle behaviors that change at run-time in response to environmental changes. This paper introduces a new extension of Reconfigurable Stoc-hastic reward Nets (RSRN), called Extension Neural Rewri-ting Petri Nets (ExNRPN), which enables the performability modeling and simulation of modern ADESs. ExNRPNs are obtained by incorporating in some special transitions of RSRNs an extension neural network (ENN) algorithm where the run-time calculation and reconfiguration is done in the local components, while the adaptation is performed for the whole system. The application of the proposed ExNRPN is illustrated by performability modeling a particular ADES. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject adaptive systems en_US
dc.subject extension neural networks en_US
dc.subject performability modeling en_US
dc.subject rewriting rule en_US
dc.subject stochatic Petri nets en_US
dc.title Performability Modeling of Self-Adaptive Systems Based on Extension Neural Rewriting Stochastic Petri Nets en_US
dc.type Article en_US


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  • 2022
    Proceedings of the 12th IC|ECCO; October 20-21, 2022

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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