The QTTI is a variant of linear multiple regression analysis and can done be used to quantify the relationships between product form elements and product images [5], while the GP model can deal with incomplete information effectively and requires only four data sets or more [16]. As such, the GP can be used to predict how a particular combination of product form elements matches a product image, particularly when the information is available only for a limited number of product form elements [10]. Due to the effective learning ability, NNs have been applied successfully in a wide range of fields, using various learning algorithms [18�C20]. NNs are well suited to formulate the product design process for matching the product form (the input variables) to the consumers’ perceptions (the output variables), which is often a black box and cannot be precisely described [10].

In subsequent sections, we first present the quantitative analysis methods used to analyze the experimental data sets for answering the research questions. Then we conduct an experimental study on PDAs to describe how Kansei Engineering can be used to extract representative samples and product form elements as numerical data sets required for analysis. Finally, we discuss the results of applying these techniques and evaluate their performance in order to determine the better model that can be used to help product designers meet consumers’ requirements for a desirable product image.2. Methods of Quantitative AnalysisIn this section, we present a brief outline of the relevant theories and algorithms, including the QTTI, the GP, and the NNs.

We use these techniques to examine the relationship between product form elements and product images.2.1. Quantification Theory Type IThe QTTI can be regarded as a method of qualitative and categorical multiple regression analysis method [15], which allows inclusion of independent variables that are categorical and qualitative in nature, such as product form elements and quantitative criterion variables within Kansei Engineering. In Kansei Engineering, product form elements are typically classified into two levels that correspond to form design element and its treatments, respectively. The QTTI consists of the followings six steps [15].Step 1 ��Define the Kansei relational model associated with the Kansei measurement scores of experimental samples with respect to an image word Drug_discovery pair. In Kansei Engineering, the criterion variables represent the product image, and the explanatory variables represent the product form elements.