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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Modeling of Dynamics Using Process State Projection on the Self Organizing Map
verfasst von : Juan J. Fuertes-Martínez, Miguel A. Prada, Manuel Domínguez-González, Perfecto Reguera-Acevedo, Ignacio Díaz-Blanco, Abel A. Cuadrado-Vega
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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In this paper, an approach to model the dynamics of multivariable processes based on the motion analysis of the process state trajectory is presented. The trajectory followed by the projection of the process state onto the 2D neural lattice of a Self-Organizing Map (SOM) is used as the starting point of the analysis. In a first approach, a coarse grain cluster-level model is proposed to identify the possible transitions among process operating conditions (clusters). Alternatively, in a finer grain neuron-level approach, a SOM neural network whose inputs are 6-dimensional vectors which encode the trajectory (T-SOM), is defined in a top level, where the KR-SOM, a generalization of the SOM algorithm to the continuous case, is used in the bottom level for continuous trajectory generation in order to avoid the problems caused in trajectory analysis by the discrete nature of SOM. Experimental results on the application of the proposed modeling method to supervise a real industrial plant are included.