Application of Improved WNN Model in GNSS Elevation Fitting
Aiming at the defects of wavelet neural network model in GNSS(Global Navigation Satellite System)elevation fitting,this paper proposes a wavelet neural network(WNN)combination model optimized by genetic simulated annealing(GSA)algorithm and adds simulated annealing(SA)algorithm to genetic algorithm(GA)population updating,then the diversity of the population is guar-anteed.The combined model makes full use of GSA's global search ability to automatically optimize WNN model parameters,so as to achieve the acquisition of global optimal solution of WNN model and the improvement of GNN elevation fitting accuracy.The experi-ment is carried out with the measured data of D-order GNSS leveling network in a certain area.The results show that the GSA-WNN model proposed in this paper has higher accuracy and stability of GNSS elevation fitting than the traditional BP neural network model and WNN model,and is more suitable for practical engineering scenarios.
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