Nonlinear Modeling of Dynamic Systems with the Self-Organizing Map

Authors:
Guilherme De A. Barreto;Aluizio F. R. Araújo
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 5
Cited 1

Modeling of dynamic systems

Modeling of dynamic systems
Nonlinear black-box modeling in system identification: a unified overview

Automatica (Journal of IFAC) - Special issue on trends in system identification
Self-organizing maps

Self-organizing maps
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook

Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Context in temporal sequence processing: a self-organizing approach and its application to robotics

IEEE Transactions on Neural Networks

A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case

Neural Computation

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Abstract

In this paper we propose an unsupervised neural modeling technique, called Vector-Quantized Temporal Associative Memory (VQTAM). Using VQTAM, the Kohonen's self-organizing map (SOM) becomes capable of approximating dynamical nonlinear mappings from time series of measured input-output data. The SOM produces modeling results as accurate as those produced by multilayer perceptron (MLP) networks, and better than those produced by radial basis functions (RBF) networks, both the MLP and the RBF based on supervised training. In addition, the SOM is less sensitive to weight initialization than MLP networks. The three networks are evaluated through simulations and compared with the linear ARX model in the forward modeling of a hydraulic actuator.