TY - GEN
T1 - Dynamic clustering and neuro-fuzzy identification for the analysis of fusion plasma signals
AU - Martin H, J. A.
AU - Santos, M.
AU - Farias, G.
AU - Duro, N.
AU - Sanchez, J.
AU - Dormido, R.
AU - Dormido-Canto, S.
AU - Vega, J.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Dynamic clustering
KW - Fusion plasma signals
KW - Fuzzy models
KW - Neuro-fuzzy identification
UR - http://www.scopus.com/inward/record.url?scp=51149109547&partnerID=8YFLogxK
U2 - 10.1109/WISP.2007.4447626
DO - 10.1109/WISP.2007.4447626
M3 - Conference contribution
AN - SCOPUS:51149109547
SN - 142440830X
SN - 9781424408306
T3 - 2007 IEEE International Symposium on Intelligent Signal Processing, WISP
BT - 2007 IEEE International Symposium on Intelligent Signal Processing, WISP
T2 - 2007 IEEE International Symposium on Intelligent Signal Processing, WISP
Y2 - 3 October 2007 through 5 October 2007
ER -