Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20320
Title: Design rule extraction from a trained ann model using ga for product form design of mobile phones
Authors: Fung, KY
Tang, CY 
Lee, EWM
Ho, GTS
Siu, MKW 
Mou, WL
Keywords: Affective design
Artificial neural network (ANN)
Product form design
Rule extraction from ANN
Issue Date: 2012
Source: Intelligent automation and soft computing, 2012, v. 18, no. 4, p. 369-379 How to cite?
Journal: Intelligent Automation and Soft Computing 
Abstract: An artificial neural network (ANN) model and rule extraction from a trained ANN using genetic algorithm (GA) are applied to predict and advise on the rules for optimal product form design for a particular customer feeling. To map design elements and the affected impressions, principal component analysis (PCA) is employed to determine the essential dimensions for data analysis. By using ANN to examine the relationships between perceptual value and form elements, black-box ANN knowledge can be extracted by applying GA to generate design rules. A case study on the product form of a mobile phone design was conducted to implement the proposed approach. The resultant rules can be used to help product designers to better understand key design elements and to verify optimal solutions suggested by using ANN models.
URI: http://hdl.handle.net/10397/20320
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