特征驱动代理加速的J-A磁滞模型参数辨识方法

doi:10.6040/j.issn.1672-3961.0.2025.167

摘要/Abstract

摘要： 针对Jiles-Atherton(J-A)磁滞模型参数辨识中进化算法迭代评估代价过高的问题,提出一种特征驱动代理加速的参数辨识方法。以六维物理特征向量替代数百维磁滞回线采样点作为辨识目标,消除样本间信息冗余;引入基于无量纲比值输入工程化的径向基函数(radial basis function, RBF)代理模型,将单次评估压缩至微秒级前向推理;提出特征驱动差分进化(feature-directed differential evolution, FD-DE)算法,以RBF解析梯度的加权多特征方向引导变异,采用反向退火策略,即搜索早期以随机探索为主,后期逐步引入梯度引导。试验采用基于取向硅钢典型参数区间构造的J-A仿真数据集验证,结果表明RBF代理模型六特征决定系数R2>0.99,输入工程化将R2从0.983 0提升至0.997 3,FD-DE较标准差分进化(differential evolution, DE)收敛加速42%,各特征辨识误差均满足磁滞回线重建精度要求。所提方法可适应不完备数据,代理模型经一次训练后可执行多种识别任务。

关键词: Jiles-Atherton模型, 参数辨识, 代理模型, 差分进化, 定向变异

Abstract: To address the prohibitive per-iteration evaluation cost of evolutionary algorithms in Jiles-Atherton(J-A)hysteresis model parameter identification, a feature-directed surrogate-accelerated parameter identification method was proposed. A six-dimensional physical feature vector replaced hundreds of B-H loop sampling points as the identification target, eliminating inter-sample redundancy. A radial basis function(RBF)surrogate model with dimensionless-ratio input engineering was introduced to compress single-evaluation time to microsecond-scale inference. A feature-directed differential evolution(FD-DE)algorithm was proposed to exploit the RBF analytical gradient via weighted multi-feature direction with reverse annealing, that was, exploration dominated early search while gradient guidance was progressively introduced. Experiments on a J-A simulated dataset constructed from typical grain-oriented electrical steel parameter ranges showed R²>0.99 for all six features of RBF surrogate model, input engineering improved R² from 0.983 0 to 0.997 3, FD-DE accelerated convergence by 42% over standard differential evolution(DE), with all feature-level errors meeting hysteresis loop reconstruction requirements. The proposed method accommodated incomplete data and the surrogate model served multiple identification tasks after one training.

Key words: Jiles-Atherton model, parameter identification, surrogate model, differential evolution, directed mutation

中图分类号:

TM271

胥建文,臧英,高腾,张志强,孟令卓超,刘志珍,孙维择. 特征驱动代理加速的J-A磁滞模型参数辨识方法[J]. 山东大学学报 (工学版), 2026, 56(5): 147-153.

Xu Jianwen, Zang Ying, Gao Teng, Zhang Zhiqiang, Meng Lingzhuochao, Liu Zhizhen, Sun Weize. Feature-directed surrogate-accelerated parameter identification method for the J-A hysteresis model[J]. Journal of Shandong University(Engineering Science), 2026, 56(5): 147-153.

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