中圖分類號(hào)： TN081；TP277
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.201259
中文引用格式： 陳路,，鄭丹,，童楚東. 基于核函數(shù)及參數(shù)優(yōu)化的KPLS質(zhì)量預(yù)測(cè)研究[J].電子技術(shù)應(yīng)用,，2021，47(12)：100-104.
英文引用格式： Chen Lu,，Zheng Dan,，Tong Chudong. The optimization of the kind and parameters of kernel function in KPLS for quality prediction[J]. Application of Electronic Technique，2021,，47(12)：100-104.

The optimization of the kind and parameters of kernel function in KPLS for quality prediction

Abstract： Kernel partial least squares(KPLS) has been widely used in industrial process monitoring and quality prediction. The choice of kernel function and kernel parameters has an important impact on the KPLS quality prediction results. However, how to choose the kernel function type and kernel parameters has always been the bottleneck of the application of this method. To solve the above problems, a kernel function optimization method based on improved genetic algorithm is proposed. In this method, the kernel type and kernel parameters are used as the optimal decision variables, and the root mean square error is targeted. It is designed in terms of coding scheme, genetic strategy, fitness function optimization, crossover and mutation algorithms to ensure the variety of kernel functions, and uses the 2-fold cross-validation method to verify the training results. The Tennessee-Eastman Process(TE) is combined with MATLAB for simulation experiments. The simulation results show that the method can find the optimal kernel function and its kernel parameters, and has good stability and consistency.

Key words : kernel partial least squares,；genetic algorithm,；quality prediction；k-fold cross-validation