Time Dependent Neural Network Models for Detecting Changes of State in Earth and Planetary Processes

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TypeArticle
ConferenceProceedings of the International Joint Conference on Neural Networks(IJCNN'05), August 1-4, 2005., Montréal, Québec, Canada
AbstractThis paper explores a computational intelligence approach to the problem of detecting internal changes in time dependent processes described by heterogeneous, multivariate time series with imprecise data and missing values. Processes are approximated by collections of time-dependent nonlinear AR models represented by a special kind of neuro-fuzzy neural networks. Grid and high throughput computing model-mining procedures using neuro-fuzzy networks and genetic algorithms, generate collections of models composed by sets of time lag terms from the time series, as well as prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its potential is revealed by experiments using paleoclimate and solar data.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48125
NPARC number5763942
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Record identifier364546d2-217e-40c3-b0a9-f635a7aaa770
Record created2009-03-29
Record modified2016-05-09
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