Utilizing Hopfield neural networks in the analysis of reluctance motors

IEEE Transactions on Magnetics

Volumn 36, Issue 5 I, 2000, Pages 3147-3149

  Adly, A A ^a,b   Abd El Hafiz, S K ^a,b  

^a IEEE   (Egypt)

^b CAIRO UNIVERSITY   (Egypt)

Author keywords

Electromagnetic field analysis; Hopfield neural networks; Reluctance motors; Synchronous machines

Indexed keywords

ELECTROMAGNETIC FIELDS; NEURAL NETWORKS; NUMERICAL ANALYSIS; SYNCHRONOUS MACHINERY;

HOPFIELD NEURAL NETWORK; ROTOR SALIENCY;

RELUCTANCE MOTORS;

EID: 0034260475     PISSN: 00189464     EISSN: None     Source Type: Journal    
DOI: 10.1109/20.908715     Document Type: Article

References (7)

1. A new design technique based on a suitable choice of rotor geometrical parameters to maximize torque and power factor in synchronous reluctance motors: Part I - Theory
2. Magnetic field and core loss evaluation of ALA-rotor synchronous reluctance machines taking into account material anisotropy
3. An improved magnetic equivalent circuit method for predicting the characteristics of highly saturated electromagnetic devices
4. Effects of airgap and magnet shapes on permanent magnet reluctance torque