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为研究模型异参同效问题产生的原因,基于SCEM-UA算法,选用似然函数L(20)=(σ2ε)-20,在实际资料及理想资料两种情况下,分析了Nash模型参数率定中的异参同效问题。结果表明,在理想资料情况下,采用SCEM-UA算法能得到参数真值,即避免了参数的异参同效性影响,可见模型异参同效的根本原因是参数之间的互补作用或相依性,而精确的输入、输出和模型结构是参数率定的关键影响因素,优化方法只有在模型结构和资料均准确的条件下才能发挥其功效,从而避免了异参同效问题。
In order to study the cause of the problem of the same-model effect of the model, based on the SCEM-UA algorithm, the likelihood function L (20) = (σ2ε) -20 was chosen to analyze the Nash model parameter rate under both actual and ideal conditions The same problem with the same effect in different countries. The results show that the true value of the parameter can be obtained by using the SCEM-UA algorithm under the ideal data, which avoids the effect of the parametric heterogeneity of the same effect. It can be seen that the root cause of the same effect of the model parameter is the complementarity or dependence of the parameters However, the precise input, output and model structure are the key factors affecting the parameter calibration. The optimization method can only exert its efficacy when the model structure and data are accurate, so as to avoid the problem of the same effect of different parameters.