Nonparametric Modeling and Parameter Optimization of Multistage Synchronous Induction Coilgun

The launching process of multistage synchronous induction coilgun (MSSICG) is a complex process involving multifield coupling, so it is time-consuming to iteratively optimize its structural parameters by stochastic optimization algorithm. A nonlinear regression modeling method of MSSICG based on mainstream machine learning methods is proposed in this paper. Rooted in the current filament model (CFM) verified by prototype experiment, the sample set for training and testing could be obtained by a hybrid experimental design method. The least-squares support vector machine (LSSVM), the kernel extreme learning machine (KELM), and the eco state network (ESN) were employed to learn the training samples. In order to improve the accuracy of the prediction model, the chicken swarm optimization (CSO) was introduced to pretrain the hyperparameters of the LSSVM and the KELM as well as the parameters of the dynamic reservoir of the ESN. The results show that the predictive modeling of MSSICG based on CSO-KELM has better accuracy and generalization performance. Based on the obtained regression model, the CSO algorithm was used to optimize the structural parameters of a five-stage coilgun. It turns out that the optimization based on nonparametric model has higher computational efficiency than the optimization method which requires large-scale iterative calculation. This provides a novel idea for the engineering design of the MSSICG.