Production rule and network structure models for knowledge extraction from complex processes under uncertainty

Boriana Vatchova; Alexander Emilov Gegov

doi:10.1007/978-3-319-41438-6_20

Production rule and network structure models for knowledge extraction from complex processes under uncertainty

Boriana Vatchova, Alexander Emilov Gegov

School of Computing

Bulgarian Academy of Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The proposed approach which can be applied in this case is based on artificial intelligence methods.

Original language	English
Title of host publication	Recent Contributions in Intelligent Systems
Editors	Vasil Sgurev, Ronald Yager, Janusz Kacprzyk , Krasimir Atanassov
Publisher	Springer
Pages	379-390
Number of pages	12
Volume	657
Edition	1
ISBN (Electronic)	978-3319414386
ISBN (Print)	978-3319414379
DOIs	https://doi.org/10.1007/978-3-319-41438-6_20
Publication status	Published - 2017

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer

Keywords

production rules
knowledge extraction
complex processes
network models

Access to Document

10.1007/978-3-319-41438-6_20Licence: Unspecified

Cite this

Vatchova, B., & Gegov, A. E. (2017). Production rule and network structure models for knowledge extraction from complex processes under uncertainty. In V. Sgurev, R. Yager, J. Kacprzyk , & K. Atanassov (Eds.), Recent Contributions in Intelligent Systems (1 ed., Vol. 657, pp. 379-390). (Studies in Computational Intelligence). Springer. https://doi.org/10.1007/978-3-319-41438-6_20

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Production rule and network structure models for knowledge extraction from complex processes under uncertainty. / Vatchova, Boriana; Gegov, Alexander Emilov.
Recent Contributions in Intelligent Systems. ed. / Vasil Sgurev; Ronald Yager; Janusz Kacprzyk ; Krasimir Atanassov . Vol. 657 1. ed. Springer, 2017. p. 379-390 (Studies in Computational Intelligence).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

TY - CHAP

T1 - Production rule and network structure models for knowledge extraction from complex processes under uncertainty

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AB - This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The proposed approach which can be applied in this case is based on artificial intelligence methods.

KW - production rules

KW - knowledge extraction

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KW - network models

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T3 - Studies in Computational Intelligence

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EP - 390

BT - Recent Contributions in Intelligent Systems

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A2 - Kacprzyk , Janusz

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Vatchova B, Gegov AE. Production rule and network structure models for knowledge extraction from complex processes under uncertainty. In Sgurev V, Yager R, Kacprzyk J, Atanassov K, editors, Recent Contributions in Intelligent Systems. 1 ed. Vol. 657. Springer. 2017. p. 379-390. (Studies in Computational Intelligence). Epub 2016 Oct 29. doi: 10.1007/978-3-319-41438-6_20

Production rule and network structure models for knowledge extraction from complex processes under uncertainty

Abstract

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Keywords

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