Abstract
A hysteresis model, based on the enhanced neural network with parallel strategy, is put forward for the prediction of the accurate magnetic behavior of electrical steel sheets (ESSs). Aimed at overcoming the drawbacks such as low convergence rate and convenient to trap into local optimum in the conventional back-propagation neural network (BPNN), a novel collaborative BPNN learning algorithm is introduced according to the error back propagation mechanism and particle swarm optimization (PSO). The reasonable selection of the test point set by the uniform design of experiment methodology, has the potential of lowering the measurement cost, together with guaranteeing the accuracy of the hysteresis modeling. A parallel strategy, which is based on the fast Fourier transformation (FFT), is applied for enhancing the train efficiency of BPNNs. The proposed algorithm is applied for the purpose of modeling the vector hysteresis behavior of ESS. Together, the comparison of the measured and predicted results of H-locus and core loss is discussed as well.
Highlights
The electric machines that have high power density and efficiency are being required in not just an electric traction system but the general industrial area as well
The behavior of the rotating magnetic fields could be analyzed with the help of a coupling vector hysteresis model with performance analysis according to the finite element method (FEM)
Some scholars have developed the vector magnetic hysteresis model in accordance with the conventional neural networks (NN) [21], [22], in the papers, the conventional feed-forward NN (FFNN) is applied, and the inherent shortcomings of the FFNN are not taken into account, for instance, easier to fall into the local minima
Summary
The electric machines that have high power density and efficiency are being required in not just an electric traction system but the general industrial area as well. Some scholars have developed the vector magnetic hysteresis model in accordance with the conventional NN [21], [22], in the papers, the conventional feed-forward NN (FFNN) is applied, and the inherent shortcomings of the FFNN are not taken into account, for instance, easier to fall into the local minima. Nh, and No refer to the numbers of the neurons in input, hidden, and output layers, correspondingly; subsequent to that, the network determined by the position vector of the ith particle could be demonstrated as hereunder: Xi = ν11, ν12, . In accordance with the weight value and the threshold value of per layer of the neural network, the location updating formula of PSO is enhanced and (9) utilized for updating the position of the particles in order to realize the optimal adjustment and updating of the network parameters.
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