DocumentCode :
1099860
Title :
Identification and control of dynamical systems using the self-organizing map
Author :
Barreto, Guilherme A. ; Araújo, Aluizio F R
Author_Institution :
Dept. of Teleinformatics Eng., Fed. Univ. of Ceara, Fortaleza-CE, Brazil
Volume :
15
Issue :
5
fYear :
2004
Firstpage :
1244
Lastpage :
1259
Abstract :
In this paper, we introduce a general modeling technique, called vector-quantized temporal associative memory (VQTAM), which uses Kohonen´s self-organizing map (SOM) as an alternative to multilayer perceptron (MLP) and radial basis function (RBF) neural models for dynamical system identification and control. We demonstrate that the estimation errors decrease as the SOM training proceeds, allowing the VQTAM scheme to be understood as a self-supervised gradient-based error reduction method. The performance of the proposed approach is evaluated on a variety of complex tasks, namely: i) time series prediction; ii) identification of SISO/MIMO systems; and iii) nonlinear predictive control. For all tasks, the simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network. The SOM has also shown to be less sensitive to weight initialization than MLP networks. We conclude the paper by discussing the main properties of the VQTAM and their relationships to other well established methods for dynamical system identification. We also suggest directions for further work.
Keywords :
MIMO systems; content-addressable storage; function approximation; identification; neurocontrollers; nonlinear dynamical systems; self-organising feature maps; MIMO systems; SISO systems; dynamical system control; dynamical system identification; function approximation; gradient based error reduction; nonlinear predictive control; self-organizing map; vector quantized temporal associative memory; Associative memory; Biological system modeling; Control system synthesis; Control systems; Function approximation; Multilayer perceptrons; Neurons; Nonlinear dynamical systems; Predictive control; System identification; Function approximation; SOMs; predictive control; self-organizing maps; temporal associative memory; time delays; time series prediction;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2004.832825
Filename :
1333086
Link To Document :
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