Application of GSA-SVM Model in GNSS Elevation Fitting
Based on the SVM model,this paper introduces genetic algorithm and simulated annealing algorithm,and proposes a new SVM model optimized by GSA.The main idea of parameter optimization of the model is as follows:firstly,the global search ability of GA is used to search the SVM parameter space,and the optimal solution is taken as the initial value of SA algorithm;secondly,it gives full play to the advantages of SA algorithm in local search,and takes the solution obtained by SA algorithm as the initial value of a new round of GA algorithm.Through the continuous iteration of global search and local search,the optimal parameters of SVM model are obtained.Using the GNSS observation data of the control point in a survey area to test the elevation fitting model proposed in this paper,the results show that compared with the traditional SVM model,the elevation fitting accuracy of the GSA-SVM model proposed in this paper is higher and has higher application value in practical engineering projects.
support vector machinegenetic algorithmsimulated annealingglobal navigation satellite systemelevation fitting
万方数据
维普
