一种基于遗传算法的极限学习机改进算法研究

The Modified Study on Extreme Learning Machine Based on Genetic Algorithm

针对传统极限学习机(ELM)缺乏有效的训练方法、应用时预测精度不理想这一弱点,提出了一种基于遗传算法(GA)训练极限学习机(GA-ELM)的方法。在该方法中,ELM的输入权值和隐藏层节点阈值映射为GA的染色体向量,GA的适应度函数对应ELM的训练误差;通过GA的遗传操作训练ELM,选出使ELM网络误差最小的输入权值和阈值,从而改善ELM的泛化性能。通过与ELM、I-ELM、OS-ELM、B-ELM4种方法的仿真结果对比,表明遗传算法有效地改善了ELM网络的预测精度和泛化能力。

In order to improve the prediction accuracy of traditional Extreme Learning Machine （ELM） algorithm. Aim to over- come the lack of effective training methods for traditional extreme learning machine （ELM）, the prediction accuracy is not ideal ~ We proposed a method which based on genetic algorithm （GA） training Extreme Learning Machine. In this method, the initial input weights and threshold vector of ELM maps to the chromosome vector of GA, and the fitness function of GA corresponds to the training error of ELM;By means of the GA to train ELM, improve the ELM＇s generalization capacity. Comparing the simulation results of GA--ELM with ELM, I--ELM, OS--ELM, B--ELM, the simulation results show that the genetic algo- rithm can effectively improve the prediction accuracy and generalization ability of ELM network.