Due to the difficulty of detection technology,and high time and economic cost,the modeling samples of soft-sensing model with difficult parameters have some problems,such as small numbers,sparse distribution,and imbalance,which seriously restrict the generalization performance of data-driven models.To solve the above prob-lems,a virtual sample generation(VSG)method based on multi-objective particle swarm optimization(MOPSO)hybrid optimization is proposed.First,the population representation mechanism of the integrated learning particle swarm optimization algorithm is designed,so that it can simultaneously encode the continuous and the discrete variables.Then,the fitness function of the integrated learning particle swarm optimization algorithm with multi-stage and multi-objective characteristics is defined to minimize the number of virtual samples while ensuring the generalization performance of the model.Finally,a multi-objective hybrid optimization task is generated for virtual samples to improve the integrated learning particle swarm optimization algorithm,so that it can adapt to the vari-able dimension characteristics of the virtual sample optimization process and improve the convergence speed.At the same time,the comprehensive evaluation index and distribution similarity index are proposed for evaluating the quality of virtual samples by referring to metric learning for the first time.In this paper,two benchmark datasets and an actual industrial dataset are used to verify the effectiveness and superiority of the proposed method.
关键词
小样本建模/虚拟样本生成/混合优化/多目标粒子群优化/分布相似度
Key words
Small sample modeling/virtual sample generation(VSG)/hybrid optimization/multi-objective particle swarm optimization(MOPSO)/distribution similarity
维普
万方数据