• 响应设计用来这些研究收集数据

    Response surface designs are used to collect data in these studies.

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  • 结果表明基于响应模型的稳健设计提高微电机质量一种有效途径

    The results shows that robust design based on response surface methodology is a promising approach to improve the quality of the micro-motor.

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  • 响应面方法计算位移设计变量莫尔积分方法近似进行了对比。

    The sensitivity of displacement with respect to design variables was compared to the approximate explicit form given by Mohr integration.

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  • 基于不同样本数响应方法寻求最佳设计大致相同区域而且CFD分析结果相比误差很小。

    The optimum design points with RSM based on different samples are almost in the same area and there is little error compared with the CFD result.

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  • 最后中心组合设计响应分析法,确定主要影响因子浓度

    Finally, the concentrations of key components were confirmed by central composite design and response surface methodology.

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  • 呈现了通过设计冲击负载主体弹性动态响应控制旋转多个实施例

    Several embodiments are presented for controlling ball spin through design of the elastic and dynamic response of the face and body under impact loading.

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  • 因素试验基础,利用因素中心组合设计原理响应分析建立二次回归模型

    Based on the results of single-factor experiments, a quadratic regression model was established with the help of two factors central composite experimental design and the response surface method.

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  • 通过响应近似模型优化,完成了高超声速巡航飞行器的多学科设计优化。

    The response surface approximation models are used to replace the original complex vehicle performance model, the optimization is conducted using a sequential quadratic programming algorithm.

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  • 采用响应设计优化实验

    Response surface methodology design was used to design and evaluate the experiment.

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  • 分别考察豆粕水分含量加热时间温度因素变化对豆粕中残余活的影响,并此基础上设计响应面实验进行参数的优化。

    The effect of factors, such as water content in meal, heating time and temperature, on enzyme activity and nitrogen solubility index were investigated, respectively.

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  • 首先通过一系列样本点有限元试验结果建立目标函数和设计变量神经网络响应模型。

    A response surface using artificial neural network is formed on the ground of initial finite element experiments.

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  • 首先通过一系列样本点有限元试验结果建立目标函数和设计变量神经网络响应模型。

    A response surface using artificial neural network is formed on the ground of initial finite element experiments.

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