Case-Based Reasoning for Product Style Construction and Fuzzy Analytic Hierarchy Process Evaluation Modeling Using Consumers Linguistic Variables

Abstract

Key form features are relative to the style of product, and the expression on style features depicts the product description and is a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. First, a style knowledge and features database are constructed using fuzzy case-based reasoning technology; a similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the fuzzy nearest-neighbor algorithm for obtaining the style knowledge extraction. Second, the linguistic variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Third, the model of product style feature set, extracted by fuzzy analytic hierarchy process (FAHP), and the final style related form features set are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product.

Keywords

• Artificial intelligent
• knowledge based systems
• machine learning algorithms
• fuzzy logic
• design methodology