传感技术学报2025，Vol.38Issue(5)：937-942,6.DOI:10.3969/j.issn.1004-1699.2025.05.025
基于麻雀搜索算法优化的深度极限学习向量机和感知阵列的毒害气体泄露检测方法研究
Research on Toxic Gas Leakage Detection Method Using Sparrow Search Algorithm Optimized Deep Extreme Learning Vector Machine and Sensing Array
摘要
Abstract
Laboratory is an important place for university teachers and students to engage in practical activities.In recent years,laborato-ry safety accidents occur frequently in universities,so laboratory safety is very important.The sensing array constructed by multiple gas sensors is arranged in the laboratory to obtain the gas detection information in the environment,nonlinear method is used to realize the pre-conditioning of perceptual signal,and support vector machine(SVM)algorithm,relevance vector machine(RVM)algorithm,K-neigh-bor(KNN)algorithm,deep extreme learning vector machine(DELM),sparrow search algorithm optimized deep extreme learning vector machine(SSA-DELM)algorithm are adopted to establish four different laboratory gas leakage classification models.The results prove that the damage detection accuracy of the sparrow search algorithm optimized deep extreme learning vector machine(SSA-DELM)algo-rithm is 95%,which is the highest prediction rate for toxic gas leakage in the laboratory.The method explored has good prediction accu-racy,which provides a new idea for toxic gas leakage detection in the laboratory.关键词
毒害气体/实验室/感知阵列/深度极限学习/麻雀搜索算法Key words
toxic gas/laboratory/sensing array/extreme learning machine/sparrow search algorithm分类
计算机与自动化引用本文复制引用
董华青,汤旭翔,孟实..基于麻雀搜索算法优化的深度极限学习向量机和感知阵列的毒害气体泄露检测方法研究[J].传感技术学报,2025,38(5):937-942,6.基金项目
国家自然科学项目(U21A20298) （U21A20298）
