New optimization design method for a double secondary linear motor based on R-DNN modeling method and MCS optimization algorithm

Abstract

Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms. However, this approach has disadvantages, e.g., the analytical model might not be accurate enough, and the intelligent optimization algorithm can easily fall into local optimization. A new linear motor optimization strategy combining an R-deep neural network (R-DNN) and modified cuckoo search (MCS) is proposed; additionally, the thrust lifting and thrust fluctuation reductions are regarded as optimization objectives. The R-DNN is a deep neural network modeling method using the rectified linear unit (RELU) activation function, and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms, particle swarm optimization, and standard CS algorithms. Finally, the validity and accuracy of this work are proven based on prototype experiments.