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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

5. Industrial Prediction Intervals with Data Uncertainty

verfasst von : Jun Zhao, Wei Wang, Chunyang Sheng

Erschienen in: Data-Driven Prediction for Industrial Processes and Their Applications

Verlag: Springer International Publishing

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Abstract

Prediction intervals (PIs) construction is a comprehensive prediction technique that provides not only the point estimates of the industrial variables, but also the reliability of the prediction results indicated by an interval. Reviewing the conventional PIs construction methods (e.g., delta method, mean and variance-based estimation method, Bayesian method, and bootstrap technique), we provide some recently developed approaches in this chapter. Here, a bootstrapping-based ESN ensemble (BESNE) model is specially proposed to produce reliable PIs for industrial time series, in which a simultaneous training method based on Bayesian linear regression is developed. Besides, to cope with the error accumulation caused by the traditional iterative mode of time series prediction, a non-iterative granular ESN is also reported for PIs construction, where the network connections are represented by the interval-valued information granules. In addition, we present a mixed Gaussian kernel-based regression model to construct PIs, in which a gradient descent algorithm is derived to optimize the hyper-parameters of the mixed Gaussian kernel. In order to tackle the incomplete testing input problem, a kernel-based high order dynamic Bayesian network (DBN) model for industrial time series is then proposed, which directly deals with the missing points involved in the inputs. Finally, we provide some case studies to verify the effectiveness of these approaches.

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Vorheriges Kapitel Factor-Based Industrial Process Prediction
Nächstes Kapitel Granular Computing-Based Long-Term Prediction Intervals
Literatur
1.
Zapranis, A., & Livanis, E. (2005). Prediction intervals for neural network models. In Proceedings of the 9th WSEAS International Conference on Computers. World Scientific and Engineering Academy and Society (WSEAS)
2.
De Veaux, R. D., Schumi, J., Schweinsberg, J., & Ungar, L. H. (1998). Prediction intervals for neural networks via nonlinear regression. Technometrics, 40(4), 273–282.MathSciNetCrossRef
3.
Hwang, J. T. G., & Ding, A. A. (1997). Prediction intervals for artificial neural networks. Journal of the American Statistical Association, 92(438), 748–757.MathSciNetCrossRef
4.
Nix, D. A., & Weigend, A. S. (1994). Estimating the mean and variance of the target probability distribution. In Proceedings of the IEEE International Conference on Neural Networks, Orlando, FL (Vol. 1, pp. 55–60).
5.
Rivals, I., & Personnaz, L. (2000). Construction of confidence intervals for neural networks based on least squares estimation. Neural Networks, 13(4–5), 463.CrossRef
6.
Ding, A., & He, X. (2003). Backpropagation of pseudo-errors: Neural networks that are adaptive to heterogeneous noise. IEEE Transactions on Neural Networks, 14(2), 253–262.CrossRef
7.
Dybowski, R., & Roberts, S. (2000). Confidence intervals and prediction intervals for feed-forward neural networks. In R. Dybowski & V. Gant (Eds.), Clinical applications of artificial neural networks. Cambridge, U.K: Cambridge University Press.
8.
Bishop, C. M. (1995). Neural networks for pattern recognition. London, UK: Oxford University Press.MATH
9.
MacKay, D. J. C. (1989). The evidence framework applied to classification networks. Neural Computation, 4(5), 720–736.CrossRef
10.
Hagan, M., & Menhaj, M. (2002). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993.CrossRef
11.
12.
Heskes, T. (1997). Practical confidence and prediction intervals. In T. P. M. Mozer & M. Jordan (Eds.), Neural information processing systems (Vol. 9, pp. 176–182). Cambridge, MA: MIT Press.
13.
Sheng, C., Zhao, J., Wang, W., et al. (2013). Prediction intervals for a noisy nonlinear time series based on a bootstrapping reservoir computing network ensemble. IEEE Transactions on Neural Networks & Learning Systems, 24(7), 1036–1048.CrossRef
14.
Tibshirani, R. (1996). A comparison of some error estimates for neural network models. Neural Computation, 8(1), 152–163.MathSciNetCrossRef
15.
Khosravi, A., Nahavandi, S., Creighton, D., et al. (2011). Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks, 22(9), 1341–1356.CrossRef
16.
Anguita, D., Ghio, A., Oneto, L., et al. (2012). In-sample and out-of-sample model selection and error estimation for support vector machines. IEEE Transactions on Neural Networks & Learning Systems, 23(9), 1390.CrossRef
17.
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, USA: Chapman & Hall.CrossRef
18.
Efron, B., & Tibshirani, R (1995). Cross-validation and the bootstrap: Estimating the error rate of a prediction rule. Dept. Stat., Stanford Univ., Stanford, CA, USA, Tech. Rep. TR-477.
19.
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.MathSciNetCrossRef
20.
Efron, B., & Tibshirani, R. (1997). Improvements on cross-validation: The .632+ bootstrap method. Journal of the American Statistical Association, 92(438), 548–560.MathSciNetMATH
21.
Xue, Y., Yang, L., & Haykin, S. (2007). Decoupled echo state networks with lateral inhibition. Neural Networks, 20(3), 365–376.CrossRef
22.
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.CrossRef
23.
Dong, R., & Pedrycz, W. (2008). A granular time series approach to long-term forecasting and trend forecasting. Physica A: Statistical Mechanics and its Applications, 387(13), 3253–3270.CrossRef
24.
Song, M., & Pedrycz, W. (2013). Granular neural networks: Concepts and development schemes. IEEE Transactions on Neural Networks & Learning Systems, 24(4), 542–553.CrossRef
25.
Cimino, A., Lazzerini, B., Marcelloni, F., et al. (2011). Granular data regression with neural networks, Fuzzy logic and applications. Lecture Notes in Computer Science, 6857, 172–179.CrossRef
26.
Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304, 78–80.CrossRef
27.
Jaeger, H. (2002). Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and echo state network approach. German National Research Center for Information Technology, GMD Rep. 159.
28.
Zhao, J., Wang, W., Liu, Y., et al. (2011). A two-stage online prediction method for a blast furnace gas system and its application. IEEE Transactions on Control Systems Technology, 19(3), 507–520.CrossRef
29.
Zhao, J., Liu, Q., Wang, W., et al. (2012). Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry. IEEE Transactions on Neural Networks and Learning Systems, 23(3), 439–450.CrossRef
30.
Liu, Y., Liu, Q., Wang, W., et al. (2012). Data-driven based model for flow prediction of steam system in steel industry. Information Sciences, 193, 104–114.CrossRef
31.
Pedrycz, W., & Homenda, W. (2013). Building the fundamentals of granular computing: A principle of justifiable granularity. Applied Soft Computing, 13(10), 4209–4218.CrossRef
32.
Jones, J. A., Evans, D., & Kemp, S. E. (2007). A note on the Gamma test analysis of noisy input/output data and noisy time series. Physica D: Nonlinear Phenomena, 229(1), 1–8.MathSciNetCrossRef
33.
Liitiainen, E., Verleysen, M., Corona, F., & Lendasse, A. (2009). Residual variance estimation in machine learning. Neurocomputing, 72(16–18), 3692–3703.CrossRef
34.
35.
Khosravi, A., Nahavandi, S., Creighton, D., et al. (2011). Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Transactions on Neural Networks, 22(3), 337–346.CrossRef
36.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (pp. 1942–1948). Piscataway: IEEE Service Center.
37.
Mackey, M. C., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300), 287–289.CrossRef
38.
De, B. K., De, B. J., Suykens, J. A., et al. (2011). Approximate confidence and prediction intervals for least squares support vector regression. IEEE Transactions on Neural Networks, 22(1), 110–120.CrossRef
39.
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer Press.MATH
40.
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.CrossRef
41.
Boyd, V., & Faybusovich, L. (2006). Convex optimization. IEEE Transactions on Automatic Control, 51(11), 1859–1859.CrossRef
42.
Wright, W. A. (1999). Bayesian approach to neural-network modeling with input uncertainty. IEEE Transactions on Neural Networks, 10(6), 1261.CrossRef
43.
Chen, L., Liu, Y., Zhao, J., Wang, W., & Liu, Q. (2016). Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks. Artificial Intelligence Review, 46, 307–326.CrossRef
44.
Fung, R., & Chang, K. C. (1990). Weighting and integrating evidence for stochastic simulation in Bayesian networks. In P. P. Bonissone, M. Henrion, L. N. Kanal, & J. F. Lemmer (Eds.), Uncertainty in artificial intelligence (Vol. 5, pp. 208–219). North Holland: Elsevier.
45.
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211–244.MathSciNetMATH
Metadaten
Titel
Industrial Prediction Intervals with Data Uncertainty
verfasst von
Jun Zhao
Wei Wang
Chunyang Sheng
Copyright-Jahr
2018
Buch
Data-Driven Prediction for Industrial Processes and Their Applications

Print ISBN: 978-3-319-94050-2

Electronic ISBN: 978-3-319-94051-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-94051-9_5