Dynamic neural networks for actuator fault diagnosis: Application to the DAMADICS benchmark problem

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 36, Issue 5, 2003, Pages 975-980

  Patan, Krzysztof ^a   Parisini, Thomas ^b  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

^b UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Actuators; Benchmark examples; Dynamic models; Fault diagnosis; Neural networks; Performance indexes; Stochastic approximation

Indexed keywords

ACTUATORS; APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; BACKPROPAGATION ALGORITHMS; BENCHMARKING; DYNAMIC MODELS; FAILURE ANALYSIS; NEURAL NETWORKS; PLANT MANAGEMENT; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SUGAR FACTORIES;

DAMADICS BENCHMARK PROBLEMS; DEVELOPMENT AND APPLICATIONS; DYNAMIC NEURAL NETWORKS; FAULT DETECTION AND ISOLATION; INDUSTRIAL CONTROL SYSTEMS; PERFORMANCE INDICES; STOCHASTIC APPROXIMATION METHODS; STOCHASTIC APPROXIMATIONS;

FAULT DETECTION;

EID: 45749085105     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/S1474-6670(17)36619-3     Document Type: Conference Paper

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