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An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing

By Jun Chen and M. Mahfouf

Abstract

In this paper, a systematic multi-objective fuzzy\ud modeling approach is proposed, which can be regarded\ud as a three-stage modeling procedure. In the first stage, an\ud evolutionary based clustering algorithm is developed to\ud extract an initial fuzzy rule base from the data. Based on\ud this model, a back-propagation algorithm with momentum\ud terms is used to refine the initial fuzzy model. The refined\ud model is then used to seed the initial population of an\ud immune inspired multi-objective optimization algorithm\ud in the third stage to obtain a set of fuzzy models with\ud improved transparency. To tackle the problem of\ud simultaneously optimizing the structure and parameters, a\ud variable length coding scheme is adopted to improve the\ud efficiency of the search. The proposed modeling approach\ud is applied to a real data set from the steel industry.\ud Results show that the proposed approach is capable of\ud eliciting not only accurate but also transparent fuzzy\ud models

Topics: G700 Artificial Intelligence
Publisher: Institution of Electronic and Electrical Engineers
Year: 2008
DOI identifier: 10.1109/GRC.2008.4664729
OAI identifier: oai:eprints.lincoln.ac.uk:2803

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