GPS elevation fitting of BP neural network optimized by genetic simulated annealing algorithm

In order to solve the problems of slow convergence speed of traditional BP neural network, easy to fall into local optimum and premature convergence of genetic algorithm optimized BP neural network (GA-BP) algorithm, a genetic simulated annealing algorithm optimized BP neural network (GSA-BP) algorithm was proposed. The simulated annealing algorithm (SA) was added to the genetic algorithm (GA) to keep the diversity of the population. GSA-BP algorithm is used to fit the elevation anomaly in a certain area, and the results are compared with BP algorithm and GA-BP algorithm. The results show that the GSA-BP algorithm can improve the accuracy by 51% and 25%, and the speed by 77% and 39% respectively, and can basically meet the requirements of the fourth grade leveling accuracy. This method proves to be feasible in GPS elevation fitting.