AGFSM: An new FSM based on adapted Gaussian membership in case retrieval model for customer-driven design

  • Authors:

  • Jin Qi;Jie Hu;YingHong Peng;Weiming Wang;Zhenfei Zhan

  • Affiliations:

  • Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

Quantified Score

Hi-index 12.05

Visualization

Abstract

In customer-driven design, reusing the design experiences of solving previous problems is a potential methodology, and the case retrieval (CR) process is a major step process, in which similarity measurement (SM) among cases is its core. However, performing the CR model with high retrieval accuracy and low computational complexity for the fuzzy, vague and imprecision customer requirements is a huge challenge for researchers and few studies attempt to research the CR model for customer-driven design. This paper proposes a new fuzzy SM (FSM) method in CR model which based on adapted Gaussian membership for customer driven design, i.e., AGFSM. In AGFSM, the adapted Gaussian membership is established based on demand information, meanwhile, the adjustment parameter is optimized via genetic algorithm (GA). Subsequently, the corresponding local similarity (LS) and global similarity (GS) are obtained. In order to find the more proper design solution, the similar case with higher suitable coefficient (SC), instead of similarity degree, is recommended as the finally design solution. Furthermore, we take power transformer design as an example to illustrate the process of the CR model with AGFSM and compare with other FSM methods to validate its superiority. As a result, the AGFSM is more efficient than previous FSM methods on the basis of retrieval accuracy and computational complexity.