Advertisement

Introduction

  • Jun Zhao
  • Wei Wang
  • Chunyang Sheng
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

This chapter gives an overall introduction to this book. First, we discuss the importance of the prediction for industrial process. Then, we divide the data-driven prediction methodology discussed in this book into a number of categories. Specifically, there are three categories, i.e., data feature-based methods, time scale-based ones, and prediction reliability-based ones. Besides, considering the characteristics of prediction modeling and industrial demands, this book introduces some commonly used prediction techniques, including the time series-based methods, the factor-based methods, the prediction intervals (PIs) construction methods, and the granular-based long-term prediction methods.

Keywords

Supervised learning Data-driven Prediction Industrial process Feature Prediction reliability Time series PIs Granular computing Long-term prediction Artificial neural networks Machine learning Data mining Support vector machines Kernel functions Fuzzy modeling 

References

  1. 1.
    Base, L. T. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.Google Scholar
  2. 2.
    Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge: MIT Press.zbMATHGoogle Scholar
  3. 3.
    Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.CrossRefGoogle Scholar
  4. 4.
    Ekkachai, K., & Nilkhamhang, I. (2016). Swing phase control of semi-active prosthetic knee using neural network predictive control with particle swarm optimization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), 1169.CrossRefGoogle Scholar
  5. 5.
    Jian, L., & Gao, C. (2013). Binary coding SVMs for the multiclass problem of blast furnace system. IEEE Transactions on Industrial Electronics, 60(9), 3846–3856.CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Teng, Y., & Zhang, Y. (2010). Complex process quality prediction using modified kernel partial least squares. Chemical Engineering Science, 65(6), 2153–2158.CrossRefGoogle Scholar
  7. 7.
    Lakehal, A., & Tachi, F. (2017). Bayesian duval triangle method for fault prediction and assessment of oil immersed transformers. Measurement and Control, 50(4), 103–109.CrossRefGoogle Scholar
  8. 8.
    Reese, B. M., & Collins, E. G., Jr. (2016). A graph search and neural network approach to adaptive nonlinear model predictive control. Engineering Applications of Artificial Intelligence, 55, 250–268.CrossRefGoogle Scholar
  9. 9.
    Jiang, S. L., Liu, M., Lin, J. H., et al. (2016). A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production. Knowledge-Based Systems, 111, 159–172.CrossRefGoogle Scholar
  10. 10.
    Fu, T. C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164–181.CrossRefGoogle Scholar
  11. 11.
    Nurkkala, A., Pettersson, F., & Saxén, H. (2011). Nonlinear modeling method applied to prediction of hot metal silicon in the ironmaking blast furnace. Industrial and Engineering Chemistry Research, 50(15), 9236–9248.CrossRefGoogle Scholar
  12. 12.
    Han, H. G., Zhang, L., Hou, Y., et al. (2016). Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Transactions on Neural Networks & Learning Systems, 27(2), 402.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Costa, A., Crespo, A., Navarro, J., et al. (2008). A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews, 12(6), 1725–1744.CrossRefGoogle Scholar
  14. 14.
    Wang, W., Pedrycz, W., & Liu, X. (2015). Time series long-term forecasting model based on information granules and fuzzy clustering. Engineering Applications of Artificial Intelligence, 41(C, 17–24.CrossRefGoogle Scholar
  15. 15.
    Khosravi, A., Nahavandi, S., Creighton, D., et al. (2011a). Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks, 22(9), 1341–1356.CrossRefGoogle Scholar
  16. 16.
    Brahim-Belhouari, S., & Bermak, A. (2004). Gaussian process for nonstationary time series prediction. Computational Statistics & Data Analysis, 47(4), 705–712.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Aye, S. A., & Heyns, P. S. (2017). An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mechanical Systems & Signal Processing, 84, 485–498.CrossRefGoogle Scholar
  18. 18.
    Keprate, A., Ratnayake, R. M. C., & Sankararaman, S. (2017). Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline. International Journal of Pressure Vessels & Piping, 153, 45–58.CrossRefGoogle Scholar
  19. 19.
    Wang, F., Su, J., & Wang, Z. (2018). Prediction of subsidence of buildings as a result of earthquakes by Gaussian process regression. Chemistry & Technology of Fuels & Oils, 99, 363–373.Google Scholar
  20. 20.
    Jani, D. B., Mishra, M., & Sahoo, P. K. (2017). Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review. Renewable & Sustainable Energy Reviews, 80, 352–366.CrossRefGoogle Scholar
  21. 21.
    Sujay, R. N., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing Journal, 19(6), 372–386.Google Scholar
  22. 22.
    Robert, C. (2012). Machine learning, a probabilistic perspective. Cambridge: MIT Press.Google Scholar
  23. 23.
    Jacobsson, H. (2005). Rule extraction from recurrent neural networks: A taxonomy and review. Neural Computation, 17(6), 1223–1263.MathSciNetCrossRefGoogle Scholar
  24. 24.
    Beyhan, S. (2017). Affine TS fuzzy model-based estimation and control of Hindmarsh-Rose neuronal model. IEEE Transactions on Systems Man & Cybernetics Systems, 47(8), 2342–2350.CrossRefGoogle Scholar
  25. 25.
    Li, Y., Zhang, W., Xiong, Q., et al. (2017). A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM. Journal of Mechanical Science & Technology, 31(6), 2711–2722.CrossRefGoogle Scholar
  26. 26.
    Wild, C. J., & Seber, G. A. F. (1989). Nonlinear regression. New York: Wiley.zbMATHGoogle Scholar
  27. 27.
    Nix, D. A., & Weigend, A. S. (1994). Estimating the mean and variance of the target probability distribution. In Proceedings of IEEE International Conference on Neural Networks (Vol. 1, pp. 55–60), Orlando, FL, 1994.Google Scholar
  28. 28.
    Pan, L., & Politis, D. N. (2016). Bootstrap prediction intervals for Markov processes. Computational Statistics & Data Analysis, 100, 467–494.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Khosravi, A., Nahavandi, S., Creighton, D., et al. (2011b). Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Transactions on Neural Networks, 22(3), 337–346.CrossRefGoogle Scholar
  30. 30.
    Sheng, C., Zhao, J., & Wang, W. (2017). Map-reduce framework-based non-iterative granular echo state network for prediction intervals construction. Neurocomputing, 222, 116–126.CrossRefGoogle Scholar
  31. 31.
    Skowron, A., & Stepaniuk, J. (2001). Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems, 16(1), 57–85.CrossRefGoogle Scholar
  32. 32.
    Pedrycz, W. (2005). Knowledge-based clustering: From data to information granules. Chichester: Wiley-Interscience.CrossRefGoogle Scholar
  33. 33.
    Lu, W., Chen, X., Pedrycz, W., et al. (2015). Using interval information granules to improve forecasting in fuzzy time series. International Journal of Approximate Reasoning, 57(1), 1–18.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhao
    • 1
  • Wei Wang
    • 1
  • Chunyang Sheng
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Shandong University of Science and TechnologyQingdaoChina

Personalised recommendations