The behavior prediction of nonlinear dynamic system is a challenging problem, especially when the system includes many independent subsystems. The observations from the complex dynamic system are the result of the interaction of multiple dynamic subsystems, which results in a loss of predictability. In this paper, semi-parametric model-based signal separation technique, in which validity function with penalizing is used to estimate the component number of the Gaussian mixture model (GMM) for every hidden source signal, is adopted to separate the observations of complex nonlinear dynamic system in order to improve its predictability. Then local support vector regression (SVR) technique is used to model the separated observations and make prediction. Finally, the prediction results are remixed as the original observation prediction or the behavior prediction of the complex nonlinear dynamic system. The experimental results show that the proposed method can separate the observation of the complex dynamic system robustly, improve the prediction accuracy substantially and perform better than the other comparison methods.

中文翻译：

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基于半参数模型的信号分离非线性动态系统的高精度行为预测

非线性动态系统的行为预测是一个具有挑战性的问题，尤其是当系统包含许多独立的子系统时。来自复杂动态系统的观测是多个动态子系统相互作用的结果，这导致了可预测性的丧失。本文采用基于半参数模型的信号分离技术，利用带惩罚的有效性函数估计每个隐藏源信号的高斯混合模型（GMM）的分量数，以分离复杂非线性的观测值。动态系统，以提高其可预测性。然后使用局部支持向量回归（SVR）技术对分离的观测值进行建模并进行预测。最后，将预测结果重新混合为原始观测预测或复杂非线性动力系统的行为预测。实验结果表明，该方法可以鲁棒地分离复杂动态系统的观测，显着提高预测精度，并优于其他比较方法。