Study and application of PSO-RBFNN model to nonlinear time series forecasting for geotechnical engineering

被引：0

作者：

Research Institute of Geotechnical Engineering, Hohai University, Nanjing 210098, China ^{[1
]}

不详 ^{[2
]}

机构：

来源：

Rock Soil Mech | 2008年 / 4卷 / 995-1000期

关键词：

Algorithms - Deformation - Forecasting - Mathematical models - Neural networks - Particle swarm optimization (PSO);

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摘要：

Due to the nonlinearity and complexity of deformation evolution of geotechnical engineering , it is difficult to describe it with simple mechanical and mathematical model. A method for forecasting the stress and displacement nonlinear time series is proposed based on constructing radial basis function neural network using particle swarm optimization algorithm .After determination of units' number in RBF layer using k-means, all parameters such as central position, shape parameter and weights of RBFNN are estimated dynamically in global with particle swarm optimization. The engineering case studies reveal that this model has high accuracy and a good prospect for nonlinear time series forecasting of geotechnical engineering.

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