Electronic nose gas concentration prediction method based on PSO-ABC-ELM

The invention provides an electronic nose gas concentration prediction method based on PSO-ABC-ELM, and the method comprises the steps: firstly carrying out the normalization and principal component analysis of data, obtaining the principal component data after dimension reduction, building an extreme learning machine, and enabling the principal component data after dimension reduction to serve asthe input of the extreme learning machine; performing embedding fusion by adopting a particle swarm algorithm and an artificial bee colony algorithm, and optimizing an input layer weight, a hidden layer weight and a hidden layer threshold of the extreme learning machine to obtain an electronic nose gas concentration prediction model; and performing concentration prediction on the gas, and inputting the test data set into the model to obtain a predicted value of the gas concentration. The method has the advantages that the advantages of a particle swarm algorithm and an artificial bee colony algorithm are combined, th