Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (22): 257-262.DOI: 10.3778/j.issn.1002-8331.2105-0199

• Engineering and Applications • Previous Articles     Next Articles

Surrogate Model Construction Method of Extreme Learning Machine Based on Transfer Learning and Application

HUANG Zeying, LI Haiyan, LIN Jingliang   

  1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2021-11-15 Published:2021-11-16



  1. 广东工业大学 机电工程学院,广州 510006


Aiming at solving the problem of using a small number of samples to construct a high-precision surrogate model in the iterative update of engineering products or the redesign and optimization of similar projects under specific requirements, a surrogate modeling method of extreme learning machine based on transfer learning is proposed. Firstly, combine the historical accumulation data of similar engineering products and a small amount of real samples sampled on the current product to construct a multi-fidelity approximate model. Then, combine the random samples generated by the approximate model and the real samples of the current product to construct the surrogate model of extreme learning machine based on transfer learning. The proposed algorithm is verified by a numerical example, and further verified by an engineering case of constructing the maximum pressure surrogate model of the hydraulic system of the boom of a forklift truck. The experimental results show that the proposed algorithm can significantly improve the prediction accuracy of the surrogate model when a small number of samples are used.

Key words: transfer learning, extreme learning machine, surrogate model, multi-fidelity



关键词: 迁移学习, 极限学习机, 代理模型, 变可信度