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Regional Models for Nonlinear System Identification Using the Self-Organizing Map

  • Amauri H. de Souza Junior
  • Guilherme A. Barreto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

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.

Keywords

Regional Model Extreme Learn Machine Hide Neuron Extreme Learn Machine Model Extreme Learn Machine Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amauri H. de Souza Junior
    • 1
  • Guilherme A. Barreto
    • 1
  1. 1.Department of Teleinformatics EngineeringFederal University of CearáFortalezaBrazil

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