| Download | - View accepted manuscript: Detecting Changes of State in Heterogeneous Dynamic Processes with Time-dependent Models: A Soft-Computing Approach (PDF, 508 KiB)
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| Author | Search for: Valdés, Julio |
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| Format | Text, Article |
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| Conference | IASTED International Joint Conference on Artificial Intelligence and Soft Computing (ASC'2004), September 1-3, 2004, Marbella, Spain |
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| Subject | neuro-fuzzy networks; evolutionary algorithms; probability distributions; heterogeneous multivariate time series; model discovery |
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| Abstract | This paper introduces a computational intelligence approach to the problem of detecting internal changes within dynamic processes described by heterogeneous, multivariate time series with imprecise data and missing values. A data mining process oriented to model discovery using a combination of neuro-fuzzy neural networks and genetic algorithms, is combined with the estimation of probability distributions and error functions associated with the set of best discovered models. The analysis of this information allows the identification of changes in the internal structure of the process, associated with the alternation of steady and transient states - zones with abnormal behaviour - instability and other situations. This approach is rather general, and its potential is revealed by experiments with simulated and real world data. |
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| Publication date | 2004 |
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| In | |
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| Language | English |
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| NRC number | NRCC 47389 |
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| NPARC number | 5764522 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 5b6b30bf-61c8-4542-869e-3ac7ffe05db8 |
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| Record created | 2009-03-29 |
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| Record modified | 2021-01-05 |
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