Reliability assessment using feed-forward neural network-based approximate meta-models
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DOI: 10.1177/1748006X11433661
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Cited by:
- Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
- Cheng Lu & Yun-Wen Feng & Cheng-Wei Fei, 2019. "Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis," Energies, MDPI, vol. 12(9), pages 1-16, April.
- Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
- Navneet Singh Bhangu & G. L. Pahuja & Rupinder Singh, 2017. "Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1923-1936, November.
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Keywords
Limit state function; feed-forward neural network; extreme learning machine; Monte Carlo simulation; reliability index;All these keywords.
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