Study on GA-based Training Algorithm for Extreme Learning Machine
Shaojian Song, Yao Wang, Xiaofeng Lin, Qingbao Huang · 2015
In view of the prediction accuracy of Extreme Learning Machine's (ELM) is affected by its input weights and hidden layer neurons thresholds, an improved training method for ELM with Genetic Algorithms (GA-ELM) is proposed in this paper. In GA-ELM, after selection, crossover and mutation of Genetic Algorithm (GA), we will get the optimal weights and thresholds, in initial which are randomly obtained by ELM, then to enhance the generalization performance of ELM. The simulation results show that, compared with other algorithms, the GA-ELM has better prediction accuracy.