Articles | Volume 14, issue 2
https://doi.org/10.5194/ms-14-361-2023
https://doi.org/10.5194/ms-14-361-2023
Research article
 | 
06 Sep 2023
Research article |  | 06 Sep 2023

Multi-objective optimization design of parallel manipulators using a neural network and principal component analysis

Chao Yang, Peijiao Li, Yang Wang, Wei Ye, Tianze Sun, Fengli Huang, and Hui Zhang

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Latest update: 08 May 2024
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Short summary
The main contribution of this work is to propose a multi-objective optimization design method in terms of factor analysis, a back-propagation (BP) neural network, principal component analysis (PCA), and a particle swarm optimization (PSO) algorithm to achieve optimal comprehensive performance of a mechanism. The computational cost of the proposed approach was reduced by 96.95 % compared with the PSO algorithm, and the comprehensive performance increased by 118.92 %.