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A novel deformation forecasting method utilizing comprehensive observation data

机译：综合观测资料的变形预测方法

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Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error an.
机译：矿山灾害往往难以预测，因此有必要找到一种有效的变形预测方法。本研究建立了变形数据和影响变形的因素数据之间的模型。这些因素包括水文地质因素和气象因素。它们之间的关系呈现出复杂的非线性关系，而这种复杂的非线性关系无法通过常规方法（例如多元线性回归）解决。随着人工智能算法的发展，人工神经网络，支持向量机和极限学习机脱颖而出。在输入数据和输出数据较少的情况下，支持向量机可以完美地建立变形预测模型。选择使用量子行为粒子群优化算法的变形预测模型来优化支持向量机。支持向量机模型的最佳配置需要由两个参数确定，即归一化均方误差an。

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