Fuzzy inference system for the characterization of SRM drives under normal and fault conditions

IEEE Transactions on Magnetics

Volumn 37, Issue 5 I, 2001, Pages 3745-3748

  Bouji, M ^b   Arkadan, A A ^a,b   Ericsen, T ^c  

^a IEEE   (United States)

^b Marquette University   (United States)

^c OFFICE OF NAVAL RESEARCH   (United States)

Author keywords

Fault analysis; Fuzzy inference systems; SRM drives

Indexed keywords

ELECTRIC DRIVES; FAILURE ANALYSIS; FINITE ELEMENT METHOD; FUZZY SETS; MATHEMATICAL MODELS; RELUCTANCE MOTORS; STATE SPACE METHODS;

FAULT ANALYSIS; FUZZY INFERENCE SYSTEM; SWITCHED RELUCTANCE MOTOR;

MAGNETIC FIELDS;

EID: 0035441309     PISSN: 00189464     EISSN: None     Source Type: Journal    
DOI: 10.1109/20.952704     Document Type: Article

References (8)

1. Switched-reluctance motor drive systems dynamic performance prediction under internal and external fault conditions
2. Modeling faulted switched reluctance motors using evolutionary neural networks
3. Neurogenetic characterization of fault tolerant switched reluctance motors
4. Fuzzy logic in control engineering
5. Performance prediction of SRM drive systems under normal and fault operating conditions using GA-based Ann method
6. Derivation of fuzzy control rules from human operator's control actions
7. ANFIS adaptive-network-based fuzzy inference system
8. Artificial intelligence models for the characterization of switched reluctance motor drive systems