Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method

Abstract

In order to solve the problem of low fault detection rate of combinatorial navigation due to the mismatch of support vector machine parameters, this paper uses genetic algorithm and lattice search method to find the optimal support vector machine penalty parameter C and kernel function parameter g. The result of the search is brought into the support vector machine to obtain the classification model and finally classify the combinatorial navigation data. The results show that the genetic algorithm has a faster search speed and a higher classification accuracy.