Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Goerke, N., Kintzler, F., Eckmiller, R. (2005). Multi-SOMs: A New Approach to Self Organised Classification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_51
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DOI: https://doi.org/10.1007/11551188_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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