A Design Support System For Cars Using Fuzzy Inverse Reasoning Integrated by Neural-Network

Abstract

In order to reduce the workload of designers and to help them stimulate ideas, several support systems for car styling design have been investigated. These systems are aimed at changing the designer's thinking process into objective models and then reasoning other cases from this model. However, many of these models have two problems; firstly, variables of the formative styling elements combine subjective scales and measurable scales, and secondly the reasoning process lacks the flexibility of that of the designer. In this paper, therefore, in order to solve these problems, the authors propose a more flexible model which improves the formative element variables, changing these variables from measurable scales to subjective scales by the use of fuzzy sets. Additionally, by the use of fuzzy inverse reasoning, broad styling solutions can be produced. A problem of the design process is that the fuzzy relational matrix contains ambiguities, and therefore a reasoning method using a neural network was introduced into the system. The process of evolving the styling from conceptual images of cars was used as a sample study in order to demonstrate the design support system developed in this paper, and by the use of practical data, the efficiency of this model was confirmed.