PRODUCT DESIGN TIME FORECAST USING RELATIVE ENTROPY KERNEL REGRESSION - Yan, Hong-Sen; Shang, Zhi-Gen (International Journal of Industrial Engineering: Theory, Applications and Practice, 2019)
Design time forecast is characterized by problems of small samples and heteroscedastic noise. For better control of product development process, the forecast method is expected to yield not only highly precise forecast values but valid forecast variances. For this end, relative entropy kernel regression (REKR) is proposed based on the combination of kernel-based regression and Gaussian margin machines (GMM). REKR maintains a Gaussian distribution over weight vectors for the kernel-based regression to minimize the combination of the relative entropy and the negative log probability densities of the targets. To simplify the formulation of REKR, the covariance matrix of Gaussian distribution is set as a diagonal matrix. The simplified optimization problem of REKR is solved based on particle swarm optimization (PSO). Having inherited the benefits of GMM, REKR can simultaneously offer the desirable forecast value and forecast variance. Experiments on the time forecast of plastic injection mold design as well as on both synthetic and real datasets do verify the feasibility and validity of REKR.