This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choic...This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choice of the unequal/equal weights.The effectiveness of the proposed technique is illustrated with a micro-drilling process.The comparison results show that the proposed technique can not only improve the model prediction performance,but also generate a reliable scheme for quality design.展开更多
基金This work was supported by the National Natural Science Foundation of China(NSFC)(grant numbers 71702072,71811540414,71401080,71871119,71771121)the Fundamental Research Funds for the Central Universities(grant number NR2019002)+1 种基金the Natural Science Foundation of Jiangsu Province-BK20170810The work of Professor Park was supported in part by the National Research Foundation of Korea grant funded by the Korea government(grant number NRF-2017R1A2B4004169).
文摘This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choice of the unequal/equal weights.The effectiveness of the proposed technique is illustrated with a micro-drilling process.The comparison results show that the proposed technique can not only improve the model prediction performance,but also generate a reliable scheme for quality design.
