自動車車体における構造知見の効率的発見のための設計支援技術の開発

（1）進化的因子抽出と因子選択確率を利用した非線形スパースモデリングの提案

抄録

We have developed the interactive design support technology in order to efficiently obtain finding of lightweight-car-body structure with high performance. The challenge with this technology is nonlinear contribution analysis with a small number of samples, a large number of variables and strong nonlinearity. In addition, complex results by nonlinear contribution analysis makes it difficult to provide engineers knowledge in weight reduction. To solve these problems, we propose the nonlinear contribution analysis method that combines the technique to derive non-linear basis functions using the evolutionary computation and sparse modelling using selection probability of factors. Additionally, we also apply the post processing that excludes factors difficult to interpret based on engineering viewpoint to analysis result. This method indicates high accuracy and robustness with a small number of samples, and the post processing decreases the number of terms in the model while maintaining high accuracy and improves interpretability. As the application result to car body structure problem, this method is shown to be effective in discovering bottlenecks and knowledge in weight reduction.