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English(EN) Regularised Iterative Generalised Least Squares with Optimal Selection of the Hyper-Parameter for Identifying Nonlinear Phenomenological Models

新方法改进了用于电池健康预测的非线性模型参数估计

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文介绍了一种用于识别非线性现象学模型的正则化迭代广义最小二乘法，该方法在锂离子电池等领域的健康状态预测中特别有用，因为这些领域的参数可能难以可靠地估计。该方法采用一种自动化的方法，在每次迭代中使用信息论度量来优化岭回归超参数，并展示了快速收敛性。该技术旨在处理异方差和序列相关数据，并通过模拟证实了其有效性。 AI

影响 该方法学可以提高各种科学和工程领域预测模型的准确性，并可能影响依赖于从实验数据中准确估计参数的AI应用。

排序理由 该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新方法改进了用于电池健康预测的非线性模型参数估计

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该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mark Cary, Charles Bokor ·

    具有最优超参数选择的正则化迭代广义最小二乘法用于识别非线性现象模型

    arXiv:2608.18742v1 Announce Type: cross Abstract: In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from e…

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