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中文摘要：
岩土工程受力变形演化是一个典型的非线性问题，其演化的高度非线性和复杂性，很难用简单的力学、数学模型描述，但可用粒子群优化径向基神经网络对岩土工程应力、位移非线性时间序列进行动态实时预测。网络径向基层的单元数通过均值聚类法确定后，所有其它参数：中心位置、形状参数、网络权值，均通过粒子群优化算法在全局空间优化确定。工程实例应用表明，该模型预测结果准确、精度高，有良好的应用前景。
英文摘要：
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 optinfization. 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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