适用于数据分类的极限学习机优化算法

Extreme Learning Machine Optimization Algorithm for Data Classification

针对极限学习机参数优化问题,提出量子遗传算法优化极限学习机的方法(QGA-ELM)。在该方法中,对ELM的输入权值和隐含层阈值采用量子比特编码,并将其映射为QGA的染色体,QGA的适应度函数为对应ELM的分类精度;通过QGA的量子旋转门优化出输入权值与隐含层阈值,以此训练出分类精度更高的ELM,从而改善ELM的泛化性能。通过ELM和QGA-ELM对数据集的仿真结果对比表明,QGA-ELM有效地提升了ELM网络的分类精度。

In order to optimize the parameters of traditional extreme learning machine(ELM),a new ELM optimized by quantum ge-netic algorithm(QGA-ELM)was proposed. In this method,the input weights and hidden layer threshold vectors of the ELM were en-coded by quantum bits and mapped to chromosomes of QGA,and the fitness function of QGA is the classification accuracy of the corre-sponding ELM. The input weights and hidden layer threshold vectors optimized by quantum rotation gate were used to train the ELM with higher classification accuracy,thereby improving the generalization performance of ELM. Comparing the simulation results of QGA-ELM and ELM,we draw the conclusion that QGA can effectively improve the classification accuracy of ELM network.