To reduce the non-uniqueness, the sensitivity coefficients and response surfaces were used to design the experiment and analyze the uniqueness of solution.
为了减少解的非唯一性，本文使用了敏感系数和响应界面对实验设计和解的唯一性进行了分析。
Based on these data the random errors' effects on the optimized parameters in the water quality model were analysed with response surfaces for objective function.
在此基础上，用绘制目标函数响应面的方法，分析水质监测数据中的随机噪声对水质模型参数最优估值的影响。
Eventually, the response surfaces composed of the CD main influence factor H1, H2 and limit drawing depth are established by the combination of GA-BP neural network and Latin Hypercube.
最后通过GA - BP神经网络与拉丁超立方抽样法相结合构建了可控拉深筋主要影响因子h1和H2与极限拉深深度之间的响应面。
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