Reliability estimation using an integrated support vector regression – variable neighborhood search model

Article


Yazdani, Maziar, Babagolzadeh, Mahla, Kazemitash, Navid and Saberi, Morteza. 2019. "Reliability estimation using an integrated support vector regression – variable neighborhood search model." Journal of Industrial Information Integration. 15, pp. 103-110. https://doi.org/10.1016/j.jii.2019.03.001
Article Title

Reliability estimation using an integrated support vector regression – variable neighborhood search model

ERA Journal ID213288
Article CategoryArticle
AuthorsYazdani, Maziar (Author), Babagolzadeh, Mahla (Author), Kazemitash, Navid (Author) and Saberi, Morteza (Author)
Journal TitleJournal of Industrial Information Integration
Journal Citation15, pp. 103-110
Number of Pages8
Year2019
PublisherElsevier BV
Place of PublicationNetherlands
ISSN2452-414X
2467-964X
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jii.2019.03.001
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S2452414X18300876
Abstract

As failure and reliability predictions play a significant role in production systems they have caught the attention of researchers. In this study, Support Vector Regression (SVR), which is known as a powerful neural network method, is developed as a way of forecasting reliability. Generally, SVR is applied in many research environments, and the results illustrate that SVR is a successful method in solving non-linear regression problems. However, SVR parameters tuning is a vital task for performing an accurate reliability estimation. We propose variable neighborhood search (VNS) for continuous space, including some simple but efficient shaking and local search as its main operators, to tune the SVR parameters and create a novel SVR-VNS hybrid system to improve the reliability of estimation accuracy. The proposed method is validated with a benchmark from the former literature and compared with conventional techniques, namely RBF (Gaussian), AR (autoregressive), MLP (logistic), MLP (Gaussian), and SVMG (SVM with genetic algorithm). The experimental results indicate that the proposed model has a superior performance for prediction reliability than other techniques.

Keywordsvariable neighborhood search (VNS), support vector regression (SVR), reliability prediction, parameter tuning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020490304. Optimisation
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Byline AffiliationsUniversity of New South Wales
School of Commerce
University of Mazandaran, Iran
Institution of OriginUniversity of Southern Queensland
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