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. © 2012 Springer-Verlag.
CITATION STYLE
De Souza, A. H., & Barreto, G. A. (2012). Regional models for nonlinear system identification using the self-organizing map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7435 LNCS, pp. 717–724). Springer Verlag. https://doi.org/10.1007/978-3-642-32639-4_85
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