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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Implementation and Identification of Preisach Parameters:Comparison Between Genetic Algorithm, Particle Swarm Optimization,and Levenberg-Marquardt Algorithm
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Implementation and Identification of Preisach Parameters:Comparison Between Genetic Algorithm, Particle Swarm Optimization,and Levenberg-Marquardt Algorithm

机译：预吸机参数的实施和识别：遗传算法，粒子群优化和Levenberg-Marquardt算法的比较

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摘要

Electromagnetic simulations of devices with soft materials and the study of the influence of cutting edge deformations/stresses on the degradation of magnetic performances require the identification of hysteresis parameters. Hence, computationally efficient implementation and identification of the hysteresis model is needed to obtain a solution with high accuracy within a reasonable time. Many studies on hysteresis identification have been reported, but few compare different methods to identify the optimal one. In the present study, we focus on the Preisach hysteresis model combined with the Lorentz modified distribution function to describe the magnetic behavior of a fully processed nonoriented Fe-3wt% Si steel sheet under static excitation. Three different identification techniques are implemented and evaluated: particle swarm optimization (PSO), genetic algorithms (GAs), and nonlinear least-squares approximation based on the Levenberg-Marquardt (LM) method. We evaluate each approach with regard to the accuracy, central processing unit computation time, and repeatability of the results. All the techniques perform well in this high-nonlinearity problem. The root-mean-square error is 3%. However, the implementation of the GA is more complex than the PSO and LM methods. The optimized parameters are obtained in a few minutes in the case of the LM method, but a few hours are required for both the other techniques. Therefore, the LM method is the most suitable technique for the identification of Preisach hysteresis.
机译：具有软材料的装置的电磁模拟以及切削刃变形/应力对磁性性能劣化的影响，需要识别滞后参数。因此，需要计算滞后模型的计算上有效的实现和识别，以在合理的时间内以高精度获得溶液。已经报道了许多关于滞后鉴定的研究，但很少比较不同的方法来识别最佳方法。在本研究中，我们专注于Preisach滞后模型与Lorentz修饰的分布功能相结合，以描述静态激发下全加工的非因子Fe-3wt％Si钢板的磁性行为。实现和评估了三种不同的识别技术：基于Levenberg-Marquardt（LM）方法的粒子群优化（PSO），遗传算法（气体），遗传算法（气体）和非线性最小二乘性。我们评估了关于精度，中央处理单元计算时间和结果的可重复性的方法。所有技术在这种高非线性问题中表现良好。根均方误差<3％。但是，GA的实现比PSO和LM方法更复杂。在LM方法的情况下，在几分钟内获得优化参数，但两种技术需要几个小时。因此，LM方法是最合适的识别Preisach滞后的技术。

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