Multi-Kernel Partial Least Squares Regression Modeling based on Adaptive Genetic Algorithm
نویسنده
چکیده
Kernel learning based soft sensor model has been focus of the machine learning domain. Kernel partial least squares (KPLS) algorithm can construct nonlinear models using the extract latent variables from the input and output data space simultaneously. However, the generalization of KPLS model relies on the model’s kernel type and kernel parameter for different modeling data. Thus, linear combination multi-kernel partial least squares regression modeling based on adaptive genetic algorithm (AGA) is proposed in this paper. Normal used global and local kernels are linear weighed to obtain mix-kernel. Kernel parameters and weighting coefficients are optimal selected using AGA algorithm. The experimental results based on the Benchmark data set show that the proposed approach is effective.
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