Deep Neural Networks for New Product Form Design

被引：2

作者：

Wei, Chun-Chun ^{[1
]}

Yeh, Chung-Hsing ^{[2
]}

Wang, Ian ^{[3
]}

Walsh, Bernie ^{[3
]}

Lin, Yang-Cheng ^{[4
]}

机构：

[1] Natl Taipei Univ Business, Dept Digital Media Design, Taoyuan 324, Taiwan

[2] Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia

[3] Monash Univ, Dept Design, Monash Art Design & Architecture, Caulfield, Vic 3145, Australia

[4] Natl Cheng Kung Univ, Dept Ind Design, Tainan 701, Taiwan

来源：

ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 2 | 2019年

关键词：

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

D O I：

10.5220/0007933506530657

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：653 / 657

页数：5

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