NEURAL-NETWORK-BASED GENETIC ALGORITHM FOR OPTIMAL KITCHEN FAUCET STYLES

摘要

Artificial neural networks (ANNs) are the models of choice in many data classification tasks. In this study, ANN classification models were used to explore user perceptions about kitchen faucet styles and investigate the relations between the overall preferences and kansei word scores of users. The scores given by consumers were obtained via a two-stage questionnaire mentioned in a previous study by the authors. Through the questionnaire, consumers were asked to give scores after examining three-dimensional (3-D) drawings of new product samples created with the help of industrial product designers. Because it was neither practical nor necessary to develop a prototype or a picture of each of the alternative designs, a fractional factorial experimental design similar to Taguchi's L-16 orthogonal array was used. After completing this preparatory work to develop ANNs and obtain the necessary related data, an analysis of variance (ANOVA) was performed to identify the critical factors that affect the accuracy of the ANN model to be used and determine the best factor levels for the ANN model. A genetic algorithm (GA) was then integrated with the ANN model found to be the best and implemented to determine the optimal levels of the design parameters related to product appearance. Lastly, the product categories were classified as unfavorable or favorable, and three products were derived for each category. In comparison with the previously published papers of the authors, the GA integrated with the ANN model was found to be an effective tool for revealing user perceptions in new product development. In regard to the findings of the present work, it can be said that, this technique can be used as an alternative of several complex analytical approaches, in order to explore users' perceptions.