Deep Neural Networks for new product form design

Chun-Chun Wei, Chung-Hsing Yeh, Ian Wong, Bernie Walsh, Yang-Cheng Lin

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Neural Networks (NNs) are non-linear models and are widely used to model complex relationships, thus being well suited to formulate the product design process for matching design form elements to consumers’ affective preferences. In this paper, we construct 36 deep NN models, using one to four hidden layers with three different dropout ratios and three widely used rules for determining the number of neurons in the hidden layer(s). As a result of extensive experiments, the NN model using one hidden layer with 140 hidden
neurons has the highest predicting accuracy rate (80%) and is used to help product designers determine the optimal form combination for new fragrance bottle design.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
EditorsOleg Gusikhin, Kurosh Madani, Janan Zaytoon
Place of PublicationPortugal
Number of pages5
ISBN (Electronic)9789897583803
ISBN (Print)9789897583803
Publication statusPublished - 2019
EventInternational Conference on Informatics in Control, Automation and Robotics 2019 - Prague, Czech Republic
Duration: 29 Jul 201931 Jul 2019
Conference number: 16th


ConferenceInternational Conference on Informatics in Control, Automation and Robotics 2019
Abbreviated titleICINCO 2019
Country/TerritoryCzech Republic
Internet address


  • Artificial Intelligence
  • Consumer-oriented Expert System
  • Deep Learning
  • Neural Networks
  • Product Form Design

Cite this