A DUAL-STAGE SAMPLING BASED ARTIFICIAL NEURAL NETWORK RESPONSE SURFACE METHODOLOGY AND ITS APPLICATION TO TUBULAR PERMANENT-MAGNET LINEAR SYNCHRONOUS MOTOR

Abstract

A dual-stage sampling (DSS) is proposed to reduce training samples of artificial neural network for establishing the analytical response surface model. The procedure of the DSS-based artificial neural network response surface (ANNRS) methodology is divided into two stages. At the rough stage, new sample is generated in the entire space by maximizing the minimum distance to existed samples. Then at the refine stage, new sample is produced in the same way, but is limited in the partial space between two samples with larger gradient and larger distance. And the efficiency of the DSS method is proved by a test function. Considering design variables about slot width and depth, permanent magnet (PM) width and height, and end tooth width, tubular permanent-magnet linear synchronous motor (TPMLSM) modeling problem is represented, whose thrust and ripple are calculated time-consumingly by finite element analysis. Then, The DSS-based ANNRS is applied to establish the TPMLSM model in good accuracy with fewer samples, lower consumption.