Construction of set of standards to improve the accuracy of expert assessments
DOI:
https://doi.org/10.15587/2313-8416.2015.41579Keywords:
time series, fuzzy logic, knowledge base, classification of anomalies, tensometryAbstract
The aim of this work is to develop an apparatus for constructing representative set of standards to improve the accuracy of detection of abnormal data sets. For this purpose in this paper we introduce the stage of constructing of set of standards of the strain-measuring signals which allow to carry out the diagnosis of time series (TS) formed the diagnosing of processes occurring in the weighing process
References
Batyrshin, I., Nedosekin, A., Stetsko, A. et. al. (2007). Fuzzy hybrid systems. Theory and practice. Moscow: FIZMATLIT, 208.
Kopytchuk, I., Kopytchuk, M., Silence, P., Mileiko, I. (2014). Building approximating fuzzy relations for determining the parameters of classification anomalies strain signals. Proc. Odessa. Polytechnic Univ. Odessa, 68–69.
Kopytchuk, M., Shendrik, E. (1999). Using the least squares method to estimate the parameters of the signal with a periodic nuisance with limited observation time. Proc. Odessa. Polytechnic Univ. Odessa, 3 (9), 167–169.
Kopytchuk, M., Shendrik, E. (2001). Increasing the accuracy of the least squares method by introducing a weighting function. Proc. Odes. Politekh Unt., 2 (14), 110–112.
Kopytchuk M, Shendrik E Study of the effectiveness of the algorithm method of least squares with a preliminary study of data transformation // Pratsі UNDІRT. Odes, 2001. - № 3 (27). - S. 72 - 74.
Kopytchuk, M., Tishyn, P., Botnari, K. (2011). Solving optimization problems for queuing systems with failures in the face of uncertainty. "The problem programuvannya". Singapore: Іnstitut software systems NAS of Ukraine, 4, 108–117.
Kopytchuk, M., Tishyn, P., Tsyurupa, M. (2013). Analysis of computer networks using a multi-level ontology of risk assessment using the methodology CORAS. "Electrical and Computer Systems", 10 (86), 120–126.
Shtovba, S. (2007). Design of fuzzy systems by means of MATLAB. Telecom, 288.
Afanasyev, T., Yarushkina, N. (2009). Fuzzy modeling of time series analysis and fuzzy trends. Ulyanovsk: UlSTU, 299.
Chandola, V. (2009). Anomaly Detection: A Survey. The University Of Minnesota, 72. Available at: http://cucis.ece.northwestern.edu/projects/DMS/publications/AnomalyDetection.pdf (Last accessed: 04.19.2014)
Downloads
Published
Issue
Section
License
Copyright (c) 2015 Николай Борисович Копытчук, Петр Метталинович Тишин, Игорь Николаевич Копытчук, Игорь Генрикович Милейко
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.
