岩石的结构是极其复杂的非连续和非均质体，其力学属性具有非线性、各向异性及随时间变化的流变特性。
The structure of rock is extremely complicated discontinuous and heterogeneous body, its mechanics property has non-linear, anisotropism and rheology characteristic, which changed over time.
根据大坝监测数据在时序上变化特征，应用了神经网络和基于遗传算法的时间序列的非线性预测模型。
Founded on change speciality of series of dam safety monitoring forecast, artificial neural networks and nonlinear models of time series based on genetic algorithms are applied.
由于干旱地区气候干燥、降水稀少、蒸发强烈，使得水文过程呈现出非常复杂的变化过程，水文时间序列表现出高度的非线性和不确定性。
In this region, dry climate, rare rain and strong evaporation make hydrological process show very complex change process, hydrological time series present highly nonlinear and uncertainty.
采用自由振荡法数值模拟了平头、钝头外形的超声速俯仰振荡时间历程，并应用奇异分解线性最小二乘法辨识稳定性导数，得到动导数随马赫数和攻角非线性变化的规律。
The dynamic derivatives conception is used as the index for the vehicle stability, but it is looked as the function of angle of attack and Mach number to induced nonlinear characteristics.
采用自由振荡法数值模拟了平头、钝头外形的超声速俯仰振荡时间历程，并应用奇异分解线性最小二乘法辨识稳定性导数，得到动导数随马赫数和攻角非线性变化的规律。
The dynamic derivatives conception is used as the index for the vehicle stability, but it is looked as the function of angle of attack and Mach number to induced nonlinear characteristics.
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