Chen, J. and Mahfouf, M. orcid.org/0000-0002-7349-5396 (2010) Interpretable fuzzy modeling using multi-objective immune-inspired optimization algorithms. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010). 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010), 18-23 Jul 2010, Barcelona, Spain. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781424469192
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 multistage 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Fuzzy logic; Indexes; Optimization; Knowledge based systems; Encoding |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Nov 2019 14:48 |
Last Modified: | 20 Nov 2019 14:45 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/FUZZY.2010.5584902 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152537 |