摘要： 针对目前电子鼻应用于气体污染物浓度检测时难以达到理想精度的问题，提出基于粒子群算法与人工蜂群算法的极限学习机(Particle Swarm Optimization and Artificial Bee Colony algorithm based Extreme Learning Machine，PSOABC-ELM)算法，通过改进极限学习机输入层与隐含层权值和隐含层阈值随机的缺陷，提高电子鼻浓度检测的精度。将PSOABC-ELM算法与其他算法进行比较，并在公开数据集上进行验证。实验结果表明，PSOABC-ELM算法用于电子鼻气体浓度检测时比其他算法精准度更高，检测结果误差更小，模型稳定性更强，为电子鼻气体浓度检测提供了一种新的方法。

中图分类号： TN02；TP212
文献标识码： A
DOI：10.16157/j.issn.0258-7998.211309
中文引用格式： 王洁，陶洋，梁志芳. 基于改进型极限学习机的电子鼻气体浓度检测[J].电子技术应用，2021，47(10)：63-67.
英文引用格式： Wang Jie，Tao Yang，Liang Zhifang. Gas concentration detection of E-nose based on improved ELM[J]. Application of Electronic Technique，2021，47(10)：63-67.

Gas concentration detection of E-nose based on improved ELM

Abstract： Aiming at unsatisfied ideal accuracy of electronic nose while testing the concentration of gas pollutants，the particle swarm optimization and artificial bee colony algorithm based extreme learning machine(PSOABC-ELM) algorithm is proposed. The accuracy of electronic nose concentration detection is enhanced by improve extreme learning machine weights of input layer and hidden layer and hidden layer threshold random defects. PSOABC-ELM is compared with other algorithms and validated on the public data set. The results show that the PSOABC-ELM algorithm perform better than the others when Testinggas concentration of electronic nose, and the detection result error is smaller and the algorithm stability is stronger, which provides a new method for the detection of gas concentration of electronic nose.

Key words : electronic nose；particle swarm optimization；artificial bee colony algorithm；extreme learning machine；concentration detection