TAILIEUCHUNG - Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper. | Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

• Vật lý
• Hopfield neural networks
• Stoner Wohlfarth like operators
• Vector hysteresis
• Hopfield neural network implementation
• Vector hysteresis
• Neural Networks
• Computer Vision
• Robot Control
• Hopfield Network
• Estimating
• Efficiency
• WWTP Biologic Treatment
• Conformable FONNs
• Fractional Lyapunov theorem
• Convex polytopic uncertainty
• Fractional exponential stability
• Exponential stability