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Original Articles

An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine

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Pages 16-24 | Received 05 May 2014, Accepted 26 Nov 2014, Published online: 27 Jan 2015

References

  • Ahmadi, M. A., M. Ebadi, A. Shokrollahi, and S. M. J. Majidi. 2013. “Evolving Artificial Neural Network and Imperialist Competitive Algorithm for Prediction Oil Flow Rate of the Reservoir.” Applied Soft Computing 13 (2): 1085–1098. doi:10.1016/j.asoc.2012.10.009.
  • Ascher, H., and H. Feingold. 1984. Repairable Systems Reliability. New York: Marcel Dekker.
  • Atashpaz-Gargari, E., and C. Lucas. 2007. “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition.” IEEE Congress on Evolutionary Computation 7: 4661–4667.
  • Azadeh, A., M. Sheikhalishahi, M. Firoozi, and S. M. Khalili. 2013. “An Integrated Multi-Criteria Taguchi Computer Simulation-DEA Approach for Optimum Maintenance Policy and Planning by Incorporating Learning Effects.” International Journal of Production Research 51 (18): 5374–5385. doi:10.1080/00207543.2013.774496.
  • Azadeh, A., M. Sheikhalishahi, S. M. Khalili, and M. Firoozi. 2014. “An Integrated Fuzzy Simulation–Fuzzy Data Envelopment Analysis Approach for Optimum Maintenance Planning.” International Journal of Computer Integrated Manufacturing 27 (2): 181–199. doi:10.1080/0951192X.2013.812804.
  • Bratton, D., and J. Kennedy. 2007. “Defining a Standard for Particle Swarm Optimization.” In Proceedings of the 2007 IEEE Swarm Intelligence Symposium, 120–127. Honolulu, HI: IEEE.
  • Cai, K.-Y., L. Cai, W.-D. Wang, Z.-Y. Yu, and D. Zhang. 2001. “On the Neural Network Approach in Software Reliability Modeling.” Journal of Systems and Software 58 (1): 47–62. doi:10.1016/S0164-1212(01)00027-9.
  • Chen, K.-Y., and C.-H. Wang. 2007. “Support Vector Regression with Genetic Algorithms in Forecasting Tourism Demand.” Tourism Management 28 (1): 215–226. doi:10.1016/j.tourman.2005.12.018.
  • Cheng, C. S., and K. K. Huang. 2014. “Applying ICA Monitoring and Profile Monitoring to Statistical Process Control of Manufacturing Variability at Multiple Locations within the Same Unit.” International Journal of Computer Integrated Manufacturing 1–12. doi:10.1080/0951192X.2013.874579.
  • Cristianini, N., and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods. Cambridge: Cambridge University Press.
  • Dohi, T., Y. Nishio, and S. Osaki. 1999. “Optimal Software Release Scheduling Based on Artificial Neural Networks.” Annals of Software Engineering 8 (1–4): 167–185. doi:10.1023/A:1018962910992.
  • Doyen, L., and O. Gaudoin. 2004. “Classes of Imperfect Repair Models Based on Reduction of Failure Intensity or Virtual Age.” Reliability Engineering and System Safety 84 (1): 45–56. doi:10.1016/S0951-8320(03)00173-X.
  • Ho, S. L., M. Xie, and T. N. Goh. 2003. “A Study of the Connectionist Models for Software Reliability Prediction.” Computers and Mathematics with Applications 46 (7): 1037–1045. doi:10.1016/S0898-1221(03)90117-9.
  • Hu, Q. P., M. Xie, S. H. Ng, and G. Levitin. 2007. “Robust Recurrent Neural Network Modeling for Software Fault Detection and Correction Prediction.” Reliability Engineering and System Safety 92 (3): 332–340. doi:10.1016/j.ress.2006.04.007.
  • Karunanithi, N., D. Whitley, and Y. K. Malaiya. 1992. “Prediction of Software Reliability Using Connectionist Models.” IEEE Transactions on Software Engineering 18 (7): 563–574. doi:10.1109/32.148475.
  • Kecman, V. 2005. “Support Vector Machines: An Introduction.” In Support Vector Machines: Theory and Applications, Vol. 1, edited by L. Wang, 1–47. Berlin: Springer.
  • Moura, M. C., and E. L. Droguett. 2009. “Mathematical Formulation and Numerical Treatment Based on Transition Frequency Densities and Quadrature Methods for Non-Homogeneous Semi-Markov Processes.” Reliability Engineering and System Safety 94 (2): 342–349. doi:10.1016/j.ress.2008.03.032.
  • Moura, M. D. C., E. Zio, I. D. Lins, and E. Droguett. 2011. “Failure and Reliability Prediction by Support Vector Machines Regression of Time Series Data.” Reliability Engineering and System Safety 96 (11): 1527–1534. doi:10.1016/j.ress.2011.06.006.
  • Mukherjee, S., E. Osuna, and F. Girosi 1997. “Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines.” In Proceedings of the 1997 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VII, 511–520. Amelia Island, FL: IEEE.
  • Muller, K. R., A. J. Smola, G. Ratsch, B. Scholkopf, and J. Kohlmorgen. 1999. Advances in Kernel Methods – Support Vector Learning, 243–254. Using support vector machines for time series prediction. Cambridge, MA: MIT Press.
  • Pai, P.-F. 2006. “System Reliability Forecasting by Support Vector Machines with Genetic Algorithms.” Mathematical and Computer Modelling 43 (3–4): 262–274. doi:10.1016/j.mcm.2005.02.008.
  • Pai, P.-F., and W.-C. Hong. 2006. “Software Reliability Forecasting by Support Vector Machines with Simulated Annealing Algorithms.” Journal of Systems and Software 79 (6): 747–755. doi:10.1016/j.jss.2005.02.025.
  • Rajabioun, R., F. Hashemzadeh, E. Atashpaz-Gargari, B. Mesgari, and F. Rajaei Salmasi. 2008. Identification of a MIMO Evaporator and Its Decentralized PID Controller Tuning Using Colonial Competitive Algorithm, 11–12. Seoul: IFAC World Congress.
  • Ross, S. M. 1993. Introduction to Probability Models. 5th ed. New York: Academic Press.
  • Sapankevych, N., and R. Sankar. 2009. “Time Series Prediction Using Support Vector Machines: A Survey.” IEEE Computational Intelligence Magazine 4 (2): 24–38. doi:10.1109/MCI.2009.932254.
  • Schölkopf, B., and A. J. Smola. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press.
  • Secchi, P., E. Zio, and F. Di Maio. 2008. “Quantifying Uncertainties in the Estimation of Safety Parameters by Using Bootstrapped Artificial Neural Networks.” Annals of Nuclear Energy 35 (12): 2338–2350. doi:10.1016/j.anucene.2008.07.010.
  • Sheikhalishahi, M. 2014. “An Integrated Simulation-Data Envelopment Analysis Approach for Maintenance Activities Planning.” International Journal of Computer Integrated Manufacturing 27 (9): 858–868. doi:10.1080/0951192X.2013.869832.
  • Sheikhalishahi, M., and S. A. Torabi. 2014. “Maintenance Supplier Selection considering Life Cycle Costs and Risks: A Fuzzy Goal Programming Approach.” International Journal of Production Research ahead-of-print 52: 7084–7099. doi:10.1080/00207543.2014.935826
  • Tian, L., and A. Noore. 2005a. “Evolutionary Neural Network Modeling for Software Cumulative Failure Time Prediction.” Reliability Engineering and System Safety 87 (1): 45–51. doi:10.1016/j.ress.2004.03.028.
  • Tian, L., and A. Noore. 2005b. “On-Line Prediction of Software Reliability Using an Evolutionary Connectionist Model.” Journal of Systems and Software 77 (2): 173–180. doi:10.1016/j.jss.2004.08.023.
  • Vapnik, V. 1995. The Nature of Statistical Learning Theory. New York: Springer Verlag.
  • Xu, K., M. Xie, L. C. Tang, and S. L. Ho. 2003. “Application of Neural Networks in Forecasting Engine Systems Reliability.” Applied Soft Computing Journal 2 (4): 255–268. doi:10.1016/S1568-4946(02)00059-5.
  • Yeh, T.-H., and S. Deng. 2012. “Application of Machine Learning Methods to Cost Estimation of Product Life Cycle.” International Journal of Computer Integrated Manufacturing 25 (4–5): 340–352. doi:10.1080/0951192X.2011.645381.
  • Yu, H., Y. Li, Q. Guo, and A. Xu. 2007. “Development and Application of Internet-Based Remote Monitoring and Diagnostic Platform for Key Equipment.” International Journal of Computer Integrated Manufacturing 20 (2–3): 234–243. doi:10.1080/09511920601150735.
  • Zio, E. 2006. “A Study of the Bootstrap Method for Estimating the Accuracy of Artificial Neural Networks in Predicting Nuclear Transient Processes.” IEEE Transactions on Nuclear Science 53 (3): 1460–1478. doi:10.1109/TNS.2006.871662.

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