The invention discloses a
gas concentration detection method, which is characterized by adopting a KPCA (
Kernel Principal Component Analysis)
algorithm for identifying a large number, firstly building two mixed kernel functions, utilizing a
vector method for building a kernel matrix, and utilizing kernel principle
component analysis for calculating characteristic vectors of the kernel matrix, wherein the
algorithm has higher recognition rate and higher arithmetic speed. The
algorithm converts a KPCA process on a
training set into a PCA process on a
data set of coordinates of all kernel training samples under a group of basis through a group of standard
orthogonal basis of a subspace expanded by the training samples in a
characteristic space, meanwhile, carries out characteristic extraction on the training samples so as to effectively capture nonlinear characteristics of training data, and is widely interested and applied in mode recognition and
regression analysis. During a solving process of KPCA, an M*M kernel matrix (M represents the number of the training samples) needs to be subjected to eigenvalue
decomposition, so that when sample characteristics are extracted, only a sample and a kernel function for forming the group of Kidd samples need to be calculated, and an experimental result verifies that the algorithm is effective.