Generating adaptation rule-specific neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10474027" target="_blank" >RIV/00216208:11320/23:10474027 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=b-~HZgiNzQ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=b-~HZgiNzQ</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10009-023-00725-y" target="_blank" >10.1007/s10009-023-00725-y</a>
Alternative languages
Result language
angličtina
Original language name
Generating adaptation rule-specific neural network
Original language description
There have been a number of approaches to employ neural networks in self-adaptive systems; in many cases, generic neural networks and deep learning are utilized for this purpose. When this approach is to be applied to improve an adaptation process initially driven by logical adaptation rules, the problem is that (1) these rules represent a significant and tested body of domain knowledge, which may be lost if they are replaced by a neural network, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic neural networks to be trained. In this paper, we introduce the rule-specific neural network method that makes it possible to transform the guard of an adaptation rule into a rule-specific neural network, the composition of which is driven by the structure of the logical predicates in the guard. Our experiments confirmed that the black box effect is eliminated, the number of weights is significantly reduced, and much faster learning is achieved whilst the accuracy is preserved. This text is an extended version of the paper presented at the ISOLA 2022 conference (Bureš et al. in Proceedings of ISOLA 2022, Rhodes, Greece, pp. 215-230, 2022).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust – Enabling trust by fluid access control to data and physical resources in Industry 4.0 systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal on Software Tools for Technology Transfer
ISSN
1433-2779
e-ISSN
1433-2787
Volume of the periodical
25
Issue of the periodical within the volume
neuveden
Country of publishing house
DE - GERMANY
Number of pages
14
Pages from-to
733-746
UT code for WoS article
001098098200001
EID of the result in the Scopus database
2-s2.0-85175982447