Support vector machine qualitative correction algorithm for multicomponent gases based on whale optimization algorithm

This paper introduces a support vector machine (SVM) multicomponent gas qualitative correction algorithm based on the whale optimization algorithm (WOA), which combines the advantages of WOA and SVM and aims to solve the cross-talk problem in multicomponent gas analysis. Through the steps of data preprocessing, feature extraction, training SVM model and prediction, the algorithm can effectively reduce the influence of cross-talk and improve the accuracy of measurement results. The experimental results show that the WOA-SVM algorithm has good robustness and generalization ability, and is an effective tool for cross-talk correction in multicomponent gas analysis.