Self-organizing maps
Using multi-SOMs and multi-neural-gas as neural classifiers
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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We propose a method to use self organizing neural networks to extract information out of nonlinear dynamic systems for control. Nonlinear strange attractors are educed by these systems or the attractors can be reconstructed. These attractors are partitioned by a newly developed self organizing neural network. Thus the stream of system states is transformed into a stream of symbols, which can now serve as basis for further investigation or control. We are convinced, that controlling and understanding such nonlinear or chaotic systems is easier, when using the information within the stream of extracted symbols.