Non-linear prediction with self-organizing maps

Jorg Walter, Helge Ritter, Klaus Schulten

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The problem of predicting highly nonlinear time sequence data, where the usual approach using adaptive, linear regressive models encounters difficulty, is considered. For this case, the use of an adaptive covering of the state space of the process with a set of linear regressive models, each of which is only locally used, is suggested. It is shown that such an adaptive covering, together with learning of the appropriate prediction coefficients, can be realized using Kohonen's algorithm of self-organizing maps. To illustrate the method, simulation results for a set of benchmarking problems are given.

Original languageEnglish (US)
Title of host publication90 Int Jt Conf Neural Networks IJCNN 90
PublisherPubl by IEEE
Number of pages6
StatePublished - 1990
Event1990 International Joint Conference on Neural Networks - IJCNN 90 - San Diego, CA, USA
Duration: Jun 17 1990Jun 21 1990


Other1990 International Joint Conference on Neural Networks - IJCNN 90
CitySan Diego, CA, USA

ASJC Scopus subject areas

  • Engineering(all)


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