Distance measures for signal processing and pattern recognition
Signal Processing
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Modern Control Engineering
Self-Organizing Maps
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Computers in Industry - Special issue: Soft computing in industrial applications
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Clustering
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Modeling of dynamics using process state projection on the self organizing map
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Self-organizing maps have been extensively used for visualization of industrial processes. Nevertheless, most of these approaches lack insight about the dynamic behavior. Recently, an approach to define visualizable maps of dynamics from data has been proposed. We propose the application of this approach to single-input single-output processes by defining several maps related to relevant features in the time-response analysis. This features are commonly used in control engineering. We show that these maps are intuitive and consistent tools for knowledge discovery and validation. They also provide a general overview of the process behavior and can be used along with other previously defined maps for process analysis and monitoring