Six sigma robust optimization method based on a pseudo single-loop strategy and RFR-DBN with insufficient samples

Design for six sigma has become increasingly important in complex optimization work considering uncertainty. In this paper, we present a six sigma robust optimization method based on a pseudo single-loop optimization strategy and an ensemble of random forest regression and deep belief networks (RFR-DBN). To verify its validity, we take a lightweight passenger car seat with insufficient samples as an example. We utilize intractable insufficient...