Abstract
Global modelling is a common approach to the problem of learning nonlinear dynamical input-output mappings. It consists in training a single multilayer neural network model using the whole dataset. On the other side of the spectrum stands the local modelling approach, in which the input space is divided into very small partitions and simpler (e.g. linear) models are trained, one per partition. In this paper, we propose a novel approach, called Regional Models (RM), that stands in between the global and local modelling ones. By following the approach by Vesanto and Alhoniemi [11], we first partition the input-output space using the Self-Organizing map (SOM), and then perform clustering over the prototypes of the trained SOM in order to find clusters of prototypes. Finally, a regional model is built for each cluster using the data vectors mapped to that cluster. The proposed approach is evaluated on two benchmarking problems and its performance is compared to those achieved by standard global and local models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barreto, G.A., Araújo, A.F.R.: Identification and control of dynamical systems using the self-organizing map. IEEE Transactions on Neural Networks 15(5), 1244–1259 (2004)
Barreto, G.A., Souza, L.G.M.: Adaptive filtering with the self-organizing maps: A performance comparison. Neural Networks 19(6), 785–798 (2006)
Chen, J.-Q., Xi, Y.-G.: Nonlinear system modeling by competitive learning and adaptive fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics-Part C 28(2), 231–238 (1998)
Cho, J., Principe, J., Erdogmus, D., Motter, M.: Quasi-sliding mode control strategy based on multiple linear models. Neurocomputing 70(4-6), 962–974 (2007)
Huang, G.B., Zhu, Q.Y., Ziew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Jain, A.K., Dubes, R.C., Chen, C.C.: Bootstrap techniques for error estimation. IEEE Transactions on Pattern Analysis and Machine Ingelligence 9(5), 628–633 (1987)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)
Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer (2000)
Peng, H., Nakano, K., Shioya, H.: A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Control Systems Technology 15(1), 130–143 (2007)
Principe, J.C., Wang, L., Motter, M.A.: Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proceedings of the IEEE 86(11), 2240–2258 (1998)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Walter, J., Ritter, H., Schulten, K.: Non-linear prediction with self-organizing map. In: Proceedings of the IEEE International Joint Conference on Neural Networks, IJCNN 1990, vol. 1, pp. 587–592 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Souza Junior, A.H., Barreto, G.A. (2012). Regional Models for Nonlinear System Identification Using the Self-Organizing Map. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_85
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)