Patterns of changes in lipids and BP were evaluated by comparing slopes over follow-up time using random coefficient linear mixed-effect models.

血脂和血压的变化模式通过比较运用随机系数线性混合效应模型得到的追踪观察期的梯度来评估。

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In comparison with BP method and RBF method, the SVM method is proved to be the best in terms of absolute error, relative error, correlation coefficient and variance.

模拟结果与BP网络方法、R BF网络方法相比，在绝对误差、相对误差、相关系数以及数据方差方面都是较优的。

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Second, a three-layer BP neural network was designed to classify the muscle movement of forearm with AR model coefficient.

其次，设计了一个三层的BP神经网络，利用AR系数对手臂的各种肢体动作进行运动模式的分类。

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