Abstract
The use of ensembles is an important way to ensure reliability of ANN-based tools. This article presents the development of an ANNs ensemble to infer steady-state in performance tests related to refrigeration compressors. Practical results are presented about the performance of individual ANNs and the ensemble. ANNs accuracy and diversity are discussed and evaluated.
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Krogh, A., Vedelsby, J.: Neural networks ensembles, cross validation and active learning. Adv. Neural Inf. Proc. Sys. 7 (1995)
Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman & Hall (1993)
Sharkey, A.J.C.: Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. Springer (1999)
Winkler, R.L., Clemen, R.T.: Multiple experts vs. multiple methods: combining correlation assessments. Decis. Anal. 1(3) (2004)
Ahmad, Z., Zhang, J.: Bayesian selective combination of multiple neural networks for im-proving long-range predictions in nonlinear process modeling. Neural Comp. & Applicat. 14 (2005)
Zio, E.: A study of the bootstrap method for estimating the accuracy of ANN in predicting nuclear transient processes. IEEE Trans. on Nucl. Sci. 53 (2006)
Peretti, C., de, S.C., Cerrato, M.: A bootstrap neural network based heterogeneous panel unit root test: application to exchange rates. Working Papers in Economics, Univ. of Glasgow (2010)
Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation, 4 (1992)
Ahmad, Z., Zhang, J.: A comparison of different methods for combining multiple neural networks models. In: Proc. of the Int. Joint Conf. on Neural Netw., vol. 1 (2002)
Edwards, P.J., Peacock, A.M., Renshaw, D., Hannah, J.M., Murray, A.F.: Minimizing risk using prediction uncertainty in neural network estimation fusion and its application to papermaking. IEEE Trans. on Neural Netw. 13(3) (2002)
Hu, Y.H., Hwang, J.: Handbook of Neural Network Signal Processing. CRC Press (2002)
Granitto, P.M., Verdes, P.F., Ceccatto, H.A.: Neural net-works ensembles: evaluation of aggregation algorithms. Artificial Intell. 163(2) (2005)
Fortuna, L., Giannone, P., Graziani, S., Xibilia, M.G.: Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery. IEEE Trans. on Instr. and Meas. 56(1) (2007)
Yu, J.B., Xi, L.F.: A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Exp. Sys. with Applicat. 36(1) (2009)
Wu, B., Yu, J.: A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes. Exp. Sys. with Applicat. 37(6) (2010)
Trichakis, I., Nikolos, I., Karatzas, G.P.: Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response. In: Hydr. Proc. (2011)
Sharkey, A.J.C., Sharkey, N.E.: How to improve the reliability of artificial neural net-works. Dept. of Computer Sci., Univ. of Sheffield, Report CS-95-11 (1995)
Zhang, J.: Developing robust non-linear models through bootstrap aggregated neural net-works. Neurocomputing 25 (1999)
Sharkey, A.J.C., Sharkey, N.E., Chandroth, G.O.: Neural nets and diversity. Neural Comp. and Applicat. 4 (1996)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and catego-rization. J. of Inf. Fusion 6 (2005)
Rokach, L.: Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography. Computational Stat. & Data Anal (2009)
ISO Testing of refrigerant compressors, ISO Standard 917 (1989)
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Penz, C.A., Flesch, C.A., Rossetto, J.P. (2015). Steady-State Inference in Performance Tests of Refrigeration Compressors Using ANN Ensemble. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds) Artificial Intelligence Applications and Innovations. AIAI 2015. IFIP Advances in Information and Communication Technology, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-319-23868-5_26
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DOI: https://doi.org/10.1007/978-3-319-23868-5_26
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