Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Internet-based remote supervision of industrial processes using self-organizing maps
Engineering Applications of Artificial Intelligence
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A hierarchical self-organizing approach for learning the patterns of motion trajectories
IEEE Transactions on Neural Networks
Application of SOM-based visualization maps for time-response analysis of industrial processes
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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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.