A diagnosis methodology for continuous time measurements using hierarchical signal representations
| Autor: | M.B. Tumer, Lee A. Belfore, Kristina M. Ropella |
|---|---|
| Přispěvatelé: | TÜMER M. B., Belfore L. A., Ropella K. |
| Rok vydání: | 2002 |
| Předmět: |
Computer science
Fuzzy set COMPUTER SCIENCE CYBERNETICS Machine learning computer.software_genre Fuzzy logic Medical diagnosis Engineering Computing & Technology (ENG) Signal processing Noise measurement Noise (signal processing) business.industry SIGNAL (programming language) Mühendislik Bilişim ve Teknoloji (ENG) Pattern recognition COMPUTER SCIENCE BİLGİSAYAR BİLİMİ SİBERNETİK Nonlinear Sciences::Cellular Automata and Lattice Gases Automaton Nonlinear system Bilgisayar Bilimi Automata theory Artificial intelligence business computer Computer Science::Formal Languages and Automata Theory |
| Zdroj: | SMC |
| DOI: | 10.1109/icsmc.1998.725127 |
| Popis: | A methodology for automated diagnosis of systems characterized by continuous signals is presented. The methodology requires the definition and construction of several fuzzy automatons each capable of identifying a particular condition. When the diagnostic system is in operation, the time sampled system measurements are presented to all automatons simultaneously. The fuzziness in automaton operation enables input processing from several perspectives, consistent with the operation of the automatons, allowing for toleration of measurement noise and other ambiguities. The methodology is applied to the problem of automatic electrocardiogram diagnosis. |
| Databáze: | OpenAIRE |
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