摘要
针对小波神经网络存在的局限性,采用粒子群算法对小波神经网络进行优化,并在此基础上建立GPS高程异常值的拟合模型。为了避免粒子群算法陷入局部极小值和收敛速度慢等问题,采用惯性权重非线性递减和自适应学习因子相结合的策略对粒子群算法进行改进,从而提高模型的训练精度。以某矿区实测GPS数据为例,对所建模型的拟合性能进行验证。结果表明,改进后的小波神经网络模型进行GPS高程拟合时具有更高的精度和稳定性。
Aiming at the limitations of wavelet neural network,we use particle swarm algorithm to optimize the wavelet neural network.On this basis,a fitting model of GPS elevation abnormality is established.In order to prevent the problems of the particle swarm algorithm from falling into local minima and slow convergence,the particle swarm algorithm is improved by using a strategy combining inertia weight non-linear decreasing and adaptive learning factor,so as to improve the training accuracy of the model.Taking the measured GPS data of a mining area as an example,we verify the fitting performance of the model.The results show that the improved wavelet neural network model has higher accuracy and stability in GPS height fitting.
作者
钱建国
樊意广
QIAN Jianguo;FAN Yiguang(School of Mapping and Geographical Science,Liaoning Technical University,88 Yulong Road,Fuxin 123000,China)
出处
《大地测量与地球动力学》
CSCD
北大核心
2022年第3期253-257,共5页
Journal of Geodesy and Geodynamics
关键词
小波神经网络
高程拟合
粒子群优化
wavelet neural network
height fitting
particle swarm optimization
