Abstract
This paper deals with the case study of usability of the Learning Entropy approach for the adaptive novelty detection in MIMO dynamical systems. The novelty detection is studied for typical parameters of linear systems including time delay. The solid-fuel combustion process is selected as a representative of typical non-linear dynamic MIMO system. The complex mathematical model of a biomass-fired 100kW boiler is used for verification of the potentials of the proposed method, and the motivation for novelty detection in solid-fuel combustion processes is discussed in this paper.
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References
Markou, M., Singh, S.: Novelty detection: a review—part 1: statistical approaches. Signal Process. 83, 2481–2497 (2003), doi:10.1016/j.sigpro.2003.07.018
Markou, M., Singh, S.: Novelty detection: a review—part 2: neural network based approaches. Signal Process. 83, 2499–2521 (2003), doi:10.1016/j.sigpro.2003.07.019
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U S A 88, 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)
Marsland, S.: Novelty detection in learning systems. Neural Comput. Surv. 3, 157–195 (2003)
Demetriou, M.A., Polycarpou, M.M.: Incipient fault diagnosis of dynamical systems using online approximators. IEEE Trans. Autom. Control 43, 1612–1617 (1998), doi:10.1109/9.728881
Trunov, A.B., Polycarpou, M.M.: Automated fault diagnosis in nonlinear multivariable systems using a learning methodology. IEEE Trans. Neural Netw. 11, 91–101 (2000), doi:10.1109/72.822513
Alippi, C., Roveri, M.: Just-in-Time Adaptive Classifiers #x2014;Part I: Detecting Nonstationary Changes. IEEE Trans. Neural Netw. 19, 1145–1153 (2008), doi:10.1109/TNN.2008.2000082
Alippi, C., Bu, L., Zhao, D.: A prior-free encode-decode change detection test to inspect datastreams for concept drift. In: 2013 Int. Jt. Conf. Neural Netw., IJCNN, pp. 1–6 (2013)
Elwell, R., Polikar, R.: Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Trans. Neural Netw. 22, 1517–1531 (2011), doi:10.1109/TNN.2011.2160459
Bukovsky, I.: Learning Entropy: Multiscale Measure for Incremental Learning. Entropy 15, 4159–4187 (2013), doi:10.3390/e15104159
Bukovsky, I., Homma, N., Cejnek, M., Ichiji, K.: Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. In: 2014 Int. Jt. Conf. Neural Netw., IJCNN, pp. 3124–3129 (2014)
Bukovsky, I., Oswald, C., Cejnek, M., Benes, P.M.: Learning entropy for novelty detection a cognitive approach for adaptive filters. Sens. Signal Process. Def. SSPD 2014, pp. 1–5 (2014)
Bukovsky, I., Kinsner, W., Bila, J.: Multiscale analysis approach for novelty detection in adaptation plot. Institution of Engineering and Technology, pp. 27–27 (2012)
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Bukovsky, I., Oswald, C. (2015). Case Study of Learning Entropy for Adaptive Novelty Detection in Solid-Fuel Combustion Control. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Intelligent Systems in Cybernetics and Automation Theory. CSOC 2015. Advances in Intelligent Systems and Computing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-319-18503-3_25
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DOI: https://doi.org/10.1007/978-3-319-18503-3_25
Publisher Name: Springer, Cham
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