@inproceedings{89bf1de6f5784b3ebe4431240a68d9d1,
title = "Neural network models for product image design",
abstract = "This paper develops four neural network models to help product developers work out a combination of product form elements for best matching a given product image. By applying four most widely used rules for determining the number of hidden neurons, these four models can be used to determine the value of the product image for a given combination of product form elements. An experimental study on mobile phones is conducted to evaluate the performance of these four models. The result of this study shows that there is no best rule for building the models and the performance of these models does not differ significantly. Although the mobile phones are chosen as the object of the experimental study, the approach presented is applicable to other products where a combination of form or other design elements is to be determined for matching a desirable product image.",
author = "Lin, {Yang Cheng} and Lai, {Hsin Hsi} and Yeh, {Chung Hsing}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004 ; Conference date: 20-09-2004 Through 25-09-2004",
year = "2004",
doi = "10.1007/978-3-540-30134-9_83",
language = "English",
isbn = "9783540232056",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "618--624",
editor = "Negoita, {Mircea Gh.} and Howlett, {Robert J.} and Jain, {Lakhmi C.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}