Abstract
Abstract. Aiming at the problem that the fitting parameters of the least squares support vector machine fitting method are difficult to select, a method of introducing the artificial bee colony algorithm into the least squares support vector machine to establish a high-precision region fitting model is proposed. The artificial bee colony algorithm can perform global tracking search on the parameters in the least squares support vector machine, imitate the honey collecting process of the bees, and use the primary value of the parameters as the honey source, and the average square error predicted by the least squares support vector machine as the target. The function value is determined by iterative update within a certain range to determine the optimal parameters, and finally a GPS height fitting model with higher precision is established. Experimental analysis, compared with the conventional least squares support vector machine fitting method, the accuracy of the fitting model constructed by the ABC-LSSVM combination method is improved by 45.4%. At the same time, the combined method is better than the particle swarm optimization fitting method and BP neural network. The legal convergence effect is higher and the stability is better. The effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS height fitting model.
Highlights
With the rapid development of modern measurement technology, the application of GPS technology in surveying and mapping work has been widely promoted, but GPS directly measures the earth's height, while the actual engineering commonly used is the normal high, there is an elevation abnormal value before the two(Tan, L
The accuracy of the model is improved by 43.3%, which fully demonstrates that the artificial bee colony optimization fitting method can improve the accuracy of the GPS fitting model
In order to solve the problem that the parameter selection is difficult when establishing the GPS height fitting model by the least squares support machine fitting method, the artificial bee colony algorithm is used to optimize the parameters of the least squares support vector machine fitting method, and through experiments and other methods
Summary
With the rapid development of modern measurement technology, the application of GPS technology in surveying and mapping work has been widely promoted, but GPS directly measures the earth's height, while the actual engineering commonly used is the normal high, there is an elevation abnormal value before the two(Tan, L. Intelligent algorithms such as particle swarm optimization, genetic algorithm and artificial neural network are often applied to the construction of GPS height fitting model, and the accuracy of the elevation anomaly value to be sought is further improved(Liu, L. Et al (2013) has proposed to use the genetic algorithm to optimize the RBF neural network to achieve the purpose of global search for the optimal radial basis function center value, so that the fitted model can better predict the elevation. This paper proposes to use artificial bee colony algorithm to optimize least squares support vector. The GPS height fitting method of the machine can use the artificial bee colony algorithm to quickly and effectively find the advantage of the optimal value, find the optimal parameter value for the least squares support vector machine within the specified range, and use a small amount of GPS level to coincide.
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