NDLI: Temporal associative memory and function approximation with the self-organizing map

Content Provider	IEEE Xplore Digital Library
Author	de A. Barreto, G. Araujo, A.F.R.
Copyright Year	2002
Description	Author affiliation: Departmento de Engenharia Eletrica, Univ. de Sao Paulo, Sao Carlos, Brazil (de A Barreto, G.; Araujo, A.F.R.)
Abstract	We propose an unsupervised neural modelling technique, called vector-quantized temporal associative memory (VQTAM), which enables Kohonen's self-organizing map (SOM) to approximate nonlinear dynamical mappings globally. A theoretical analysis of the VQTAM scheme demonstrates that the approximation error decreases as the SOM training proceeds. The SOM is compared with standard MLP and RBF networks in the forward and inverse identification of a hydraulic actuator. 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; both the MLP and the RBF being supervised algorithms. The SOM is also less sensitive to weight initialization than MLP networks. The paper is concluded with a brief discussion on the main properties of the VQTAM approach.
Sponsorship	IEEE Signal Process. Soc. IEEE Neural Network Council Swiss Nat. Sci. Found
Starting Page	109
Ending Page	118
File Size	409866
Page Count	10
File Format	PDF
ISBN	0780376161
DOI	10.1109/NNSP.2002.1030022
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-06
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Function approximation Biological system modeling Hydraulic actuators Nonlinear dynamical systems Mathematical model Predictive models Nonlinear control systems Radial basis function networks Roentgenium
Content Type	Text
Resource Type	Article

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