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Normalized neighborhood component feature selection and feasible-improved weight allocation for input variable selection

机译：归一化邻域组件特征选择和可行改进的输入变量选择权重分配

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摘要

In design optimization, as the number of input variables increases, the convergence rate of optimization tends to decrease, and the number of function calls and design change costs tend to increase. Neighborhood component feature selection (NCFS) was adopted to select significant input variables. However, the parameter determination process of the NCFS incurs a high computational cost and weakens robustness. Therefore, this study proposes a normalized NCFS (nNCFS) by normalizing scales between mean loss and regularization terms via the initial dataset Additionally, in the case of a multi-response system, complex decision-making processes that involve the allocation of weights for multiple responses are required. It is possible to allocate weights by using conventional methods such as the analytic hierarchy process and entropy methods. However, the analytic hierarchy process method is highly influenced by the designer's subjectivity, and the entropy method is unable to consider a design optimization problem. Accordingly, the feasible-improved weight allocation (FIWA) method is now proposed by considering a design optimization problem objectively. Comparing the NCFS with the nNCFS through mathematical examples, we found that the nNCFS significantly improved the computational cost and robustness. Moreover, the FIWA method selected significant input variables that yielded feasible and improved designs. Then, the nNCFS and the FIWA methods were applied to the design of the body-in-white of a vehicle. The significance of input variables was analyzed using the nNCFS, and feasible and improved designs were obtained on the basis of the significant input variables selected using the FIWA method. (c) 2021 Elsevier B.V. All rights reserved.
机译：在设计优化中，随着输入变量的数量增加，优化的收敛速度趋于降低，并且功能呼叫数量和设计变化成本趋于增加。采用邻域组件特征选择（NCFS）选择显着的输入变量。然而，NCFS的参数确定过程会引起高计算成本并削弱稳健性。因此，本研究通过初始数据集在多响应系统的情况下，通过初始数据集归一化平均数据集之间的规模来建议归一化的NCFS（NNCF），该方法涉及用于多重响应的权重分配权重的复杂决策过程是必要的。可以使用诸如分析层次结构和熵方法的传统方法来分配权重。然而，分析层次处理方法受到设计者主观性的高度影响，熵方法无法考虑设计优化问题。因此，现在通过考虑设计优化问题来提出可行改善的权重分配（FIWA）方法。通过数学例子将NCF与NNCF进行比较，我们发现NNCFS显着提高了计算成本和鲁棒性。此外，FIWA方法选择了显着的输入变量，从而产生了可行和改进的设计。然后，将NNCF和FIWA方法应用于车辆的身体的设计。使用NNCFS分析输入变量的重要性，并且基于使用FIWA方法选择的显着输入变量获得可行和改进的设计。 （c）2021 elestvier b.v.保留所有权利。

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