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A method of simplified modeling based on kernel function principal component analysis

机译：一种基于内核函数主成分分析的简化建模方法

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摘要

As the complexity of system increases, the calculation in the control process has grown in index. It would effect the stability and control precision of system. In this paper input character vectors are extracted based on kernel function principal component analysis, input space dimension is simplified and input vector space is reconstructed. Linear regression is completed by support vector machine and simplified model of control system is built. By controlling beam and ball control system, the result indicates the complexity of system based on kernel function principal component analysis has decreased, also control precision and general ability are improved. The experimental results show that the method is very effective.
机译：随着系统的复杂性增加，控制过程中的计算在索引中生长。它将实现系统的稳定性和控制精度。在本文的输入字符向量中，基于内核功能主分量分析提取，简化了输入空间尺寸并重建输入矢量空间。通过支持向量机完成线性回归，构建了控制系统的简化模型。通过控制光束和球控制系统，结果表明了基于核函数主成分分析的系统的复杂性降低，还有控制精度和一般能力得到改善。实验结果表明，该方法非常有效。

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