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首页> 外文期刊>Computer physics communications >Stochastic optimization for the calculation of the optimal critical curve from experimental data in a model of the process of regaining balance after perturbation from quiet stance
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Stochastic optimization for the calculation of the optimal critical curve from experimental data in a model of the process of regaining balance after perturbation from quiet stance

机译：随机优化用于从实验数据中计算出最佳姿态的最佳临界曲线，该模型是从安静姿态扰动后恢复平衡的过程模型

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摘要

We demonstrate the successful application of ALOPEX stochastic optimization to the problem of calculating the optimal critical curve in a dynamical systems model of the process of regaining balance after perturbation from quiet stance. Experimental data provide the time series of angles for which the subjects were able to regain balance after an initial perturbation. The optimal critical curve encloses all data points and has a minimum distance from the border points of the data set. We demonstrate the results of the optimization firstly using the traditional cost function of chi-square distance. We then successfully introduce a modified cost function that fits the model to the experimental data by taking into account the specific requirements of the model. By use of the proposed cost function, combined with the efficiency of our optimization method, an optimal critical curve is calculated even in the cases of very asymmetric data sets that lie within the capabilities of the existing model. (C) 2008 Elsevier B.V. All rights reserved.
机译：我们证明了ALOPEX随机优化方法在动态系统模型中计算最优临界曲线的成功应用，该动力学系统模型从安静的姿态中获得干扰后恢复平衡。实验数据提供了受试者在初始干扰后能够恢复平衡的角度时间序列。最佳临界曲线包含所有数据点，并且距数据集的边界点的距离最小。首先，我们使用传统的卡方距离成本函数证明了优化的结果。然后，我们通过考虑模型的特定要求，成功地引入了一个使模型适合实验数据的修正成本函数。通过使用建议的成本函数，结合我们的优化方法的效率，即使在非常不对称的数据集位于现有模型能力范围内的情况下，也可以计算出最佳临界曲线。 （C）2008 Elsevier B.V.保留所有权利。

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