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用状态空间建模进行非图形时差预测:算法开发和验证研究

Eric Pulick1, John Curtin2, Yonatan Mintz1

  • 1Department of Industrial and Systems Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States.

JMIR formative research
|June 3, 2025
PubMed
概括

与传统的机器学习相比,国家空间模型 (SSM) 对精神健康状况,如酒精使用障碍 (AUD) 的风险预测优越. 这些特征模型可以个性化患者护理,在有足够的数据的情况下改善结果.

关键词:
酒精使用障碍 饮酒障碍数字健康数字健康数字治疗学数字治疗学移动健康 移动健康 移动健康 移动健康心理健康 心理健康移动健康的移动健康个性化医疗是个性化的医疗.药物使用障碍物使用障碍物.

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Transfer Function to State Space01:23

Transfer Function to State Space

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