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中文摘要：
提出一种改进的粒子群算法（IPSO）。该算法在粒子群速度调整中加入了邻域最优粒子影响，并引入惯性权重非线性递减策略和学习因子动态调整方法。将改进的粒子群算法与BP神经网络结合形成IPSO—BP模型，并应用于大坝多源变形监测数据的预报与反演。结果表明，IPSO—BP模型收敛速度更快，有效提高了大坝多源监测数据的预报与反演能力。
英文摘要：
In this paper, an improved influence of neighborhood optimal particle particle swarm optimization （IPSO）was proposed. In the algorithm, the was considered, nonlinear inertia weight decreasing strategy and learning factor dynamic adjustment method was introduced, the dam prediction and inversion of multi-source monitoring data was carried using the IPSO_BP model which was formed by the improved IPSO combined with BP neural network. The results show that the convergence speed of IPSOBP mode/is faster, the new model can effectively improve the ability of prediction and inversion of dam multi-source monitoring data.
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