ABSTRACT A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.

中文翻译：

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核主成分分析与神经网络相结合的过程间接控制

摘要 提出了一种用于非线性离散系统控制的新的自适应核主成分分析 (KPCA)。所提出的方法可以被视为数据预处理技术的新命题。实际上，神经网络控制器的输入向量是通过 KPCA 方法进行预处理的。然后，将得到的约简神经网络控制器应用于间接自适应控制。以单输入单输出非线性离散系统和多输入多输出的时变参数情况为例，讨论了输入数据预处理对神经网络控制器结果精度的影响。系统。得出的结论是，使用 KPCA 方法，控制误差和识别误差得到显着降低。