Deep Neural Networks for New Product Form Design

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

2019

Abstract

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.

Paper Citation

in Harvard Style

Wei C., Yeh C., Wang I., Walsh B. and Lin Y. (2019). Deep Neural Networks for New Product Form Design.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-380-3, pages 653-657. DOI: 10.5220/0007933506530657

in Bibtex Style

@conference{icinco19,
author={Chun-Chun Wei and Chung-Hsing Yeh and Ian Wang and Bernie Walsh and Yang-Cheng Lin},
title={Deep Neural Networks for New Product Form Design},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2019},
pages={653-657},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007933506530657},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Deep Neural Networks for New Product Form Design
SN - 978-989-758-380-3
AU - Wei C.
AU - Yeh C.
AU - Wang I.
AU - Walsh B.
AU - Lin Y.
PY - 2019
SP - 653
EP - 657
DO - 10.5220/0007933506530657