title: An NN-based SRD decomposition algorithm and its application in nonlinear compensation.
abstract: In this study, a neural network-based square root of descending (SRD) order decomposition algorithm for compensating for nonlinear data generated by sensors is presented. The study aims at exploring the optimized decomposition of data 1.00,0.00,0.00 and minimizing the computational complexity and memory space of the training process. A linear decomposition algorithm, which automatically finds the optimal decomposition N and reduces the training time to 1/√N and memory cost to 1/N has been implemented on nonlinear data obtained from an encoder. Particular focus is given to the theoretical access of estimating the numbers of hidden nodes and the precision of varying the decomposition method. Numerical experiments are designed to evaluate the effect of this algorithm. Moreover, a designed device for angular sensor calibration is presented. We conduct an experiment that samples the data of an encoder and compensates for the nonlinearity of the encoder to testify this novel algorithm.
authors.affiliation: School of Automation, Beijing Institute of Technology, Haidian District Zhongguancun South Street No. 5, Beijing 100081, China. yanhonghang0803@gmail.com.
authors.forename: Honghang
authors.initials: H
authors.lastname: Yan
authors.affiliation: School of Automation, Beijing Institute of Technology, Haidian District Zhongguancun South Street No. 5, Beijing 100081, China. dengfang@bit.edu.cn.
authors.forename: Fang
authors.initials: F
authors.lastname: Deng
authors.affiliation: School of Automation, Beijing Institute of Technology, Haidian District Zhongguancun South Street No. 5, Beijing 100081, China. sunjian@bit.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.affiliation: School of Automation, Beijing Institute of Technology, Haidian District Zhongguancun South Street No. 5, Beijing 100081, China. chenjie@bit.edu.cn.
authors.forename: Jie
authors.initials: J
authors.lastname: Chen