توسعه یک سیستم پشتیبانی طراحی برای فرم بیرونی کفش های استفاده شده با استفاده از حداقل مربعات جزئی و شبکه های عصبی

Therefore, validation results from PCA-NN models are relatively poor. If the correlation between variables is included with PLS, the predictive capability of NNs can be effectively enhanced. The results show that PLS-NN performance in training and validation is superior to PCA-NN. Furthermore, the best predictive result is obtained from the Kansei adjective pair “clumsy-agile.” The prediction accuracy rate of the PLS-NN model improves with increasing reduction performance for PLS. The predictive system developed using PLS-NN maintains stability or robustness for linear data processing and predictive capability with non-linear data. This allows sports shoe designers to provide the best reference combinations for specific product images when creating conceptual designs of novel shoe appearance. The system can present an appropriate external form of running shoe in the user interface as long as an adjective target value is provided by the designer.