Dynamic clustering and neuro-fuzzy identification for the analysis of fusion plasma signals

J. A. Martin H, M. Santos, GONZALO ALBERTO FARIAS CASTRO, N. Duro, J. Sanchez, R. Dormido, S. Dormido-Canto, J. Vega

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Measurements in long pulse devices like ITER require the use of intelligent techniques to detect interesting events and anomalous behaviors within a continuous data flow. This detection will trigger the execution of some experimental procedures such as: increasing sampling rates, starting data sampling in additional channels or notifying the event to other diagnostics. In a first approach, an interesting event can be any non-average behavior in the expected temporal evolution of the waveforms. Therefore, a model of the signals is needed. In this work, a model that represents each type of plasma signal is obtained by means of fuzzy inference systems (FIS) which are generated by applying adaptive neuro-fuzzy techniques. The purpose of this neuro-fuzzy modeling is to identify patterns of these groups of data to produce a concise representation of a signal. Previously the signals have been preprocessed and a new dynamic clustering strategy based on a partitioning method has been applied to obtain the clusters. Off-line analyses have been applied to bolometric signals of the fusion device TJ-II Stellator with encouraging results.

Original languageEnglish
Title of host publication2007 IEEE International Symposium on Intelligent Signal Processing, WISP
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE International Symposium on Intelligent Signal Processing, WISP - Alcala de Henares, Spain
Duration: 3 Oct 20075 Oct 2007

Publication series

Name2007 IEEE International Symposium on Intelligent Signal Processing, WISP

Conference

Conference2007 IEEE International Symposium on Intelligent Signal Processing, WISP
CountrySpain
CityAlcala de Henares
Period3/10/075/10/07

Keywords

  • Dynamic clustering
  • Fusion plasma signals
  • Fuzzy models
  • Neuro-fuzzy identification

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