Location of Repository

Interpretable fuzzy modeling using multi-objective immune-inspired optimization algorithms

By Jun Chen and Mahdi Mahfouf

Abstract

Abstract—In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data

Topics: H131 Automated Engineering Design, H650 Systems Engineering, H130 Computer-Aided Engineering
Publisher: IEEE
Year: 2010
DOI identifier: 10.1109/FUZZY.2010.5584902
OAI identifier: oai:eprints.lincoln.ac.uk:2871
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://eprints.lincoln.ac.uk/2... (external link)
  • http://dx.doi.org/10.1109/FUZZ... (external link)
  • http://eprints.lincoln.ac.uk/2... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.