Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Computers in Industry - Special issue: Soft computing in industrial applications
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Qualitative modelling of time series using self-organizing maps: application to animal science
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Self organizing map (SOM) approach for classification of power quality events
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Advanced analysis methods for 3G cellular networks
IEEE Transactions on Wireless Communications
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Visualization of changes in process dynamics using self-organizing maps
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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
Self-organizing maps of nutrition, lifestyle and health situation in the world
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Monitoring industrial processes with SOM-based dissimilarity maps
Expert Systems with Applications: An International Journal
Visual analysis of a cold rolling process using a dimensionality reduction approach
Engineering Applications of Artificial Intelligence
Environmental Modelling & Software
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The self-organizing map (SOM) constitutes a powerful method for exploratory analysis of process data that is based on the so-called dimension reduction approach. The SOM algorithm defines a smooth non-linear mapping from a high-dimensional input space onto a low-dimensional output space (typically 2D) that preserves the most significant information about the input data distribution. This mapping can be used to obtain 2D representations (component planes, u-matrix, etc.) of the process variables that reveal the main static relationships, allowing to exploit available data and process-related knowledge in an efficient way for supervision and optimization purposes. In this work we present a complementary methodology to represent also the process dynamics in the SOM visualization, using maps in which every point represents a local dynamical behavior of the process and that, in addition, are consistent with the component planes of the process variables. The proposed methodology allows in this way to find relationships between the process variables and the process dynamics, opening important ways for the exploratory analysis of the dynamic behavior in non-linear and non-stationary processes. Experimental results from real data of two different industrial processes are also described, showing the possibilities of the proposed approach.