Neural networks for optimal form design of personal digital assistants

  • Authors:

  • Chen-Cheng Wang;Yang-Cheng Lin;Chung-Hsing Yeh

  • Affiliations:

  • Department of Computer Simulation and Design, Shih Chien University, Kaohsiung, Taiwan;Department of Arts and Design, National Dong Hwa University, Hualien, Taiwan;Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia

  • Venue:
  • ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
  • Year:
  • 2008

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Abstract

This paper presents a neural network (NN) approach to determining the optimal form design of personal digital assistants (PDAs) that best matches a given set of product images perceived by consumers. 32 representative PDAs and 9 design form elements of PDAs are identified as samples in an experimental study to illustrate how the approach works. Four NN models are built with different hidden neurons in order to examine how a particular combination of PDA form elements matches the desirable product images. The performance evaluation result shows that the number of hidden neurons has no significant effect on the predictive ability of the four NN models. The NN models can be used to construct a form design database for supporting form design decisions in a new PDA product development process.