Application of PSO-BP Model in Deformation Prediction Analysis of Reservoir Dams
In view of the disadvantages of BP neural network prediction model, such as slow convergence speed and local extreme val-ue, it is difficult to accurately predict the deformation trend of reservoir dams. Particle swarm optimization algorithm is used to opti-mize it and build a PSO-BP neural network model. Taking the 30 periods of consecutive monitoring data of dam settlement of a reser-voir as the data source and the first 24 periods of data as the training basis, the subsequent 6 periods of settlement data are predicted. In order to analyze and study the reliability of the prediction results of the PSO-BP neural network model, GM(1,1) model, BP neu-ral network model and PSO-BP neural network model were respectively used to predict and analyze the deformation trend of reservoir dam. The experimental results showed that the root-mean-square errors of the three prediction models were 0. 3574 mm, 0.2550 mm and 0.1783 mm, respectively. The optimized prediction model was relatively more accurate. Therefore, PSO-BP pre-diction model can reflect the deformation trend of reservoir dam more accurately.
BP neural networkparticle swarm algorithmreservoir dam monitoringdeformation prediction
万方数据
维普
