Granular Computing and Evolutionary Fuzzy Modelling for Mechanical Properties of Alloy Steels
| Authors: | Mahfouf Mahdi, The University of Sheffield, United Kingdom
Panoutsos George, The University of Sheffield, United Kingdom |
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| Topic: | 6.2 Mining, Mineral & Metal Processing |
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| Session: | Rolling Mills II |
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| Keywords: | Fuzzy systems, model approximation, data reduction, knowledge acquisition, genetic algorithms, machine learning |
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Abstract
In this paper the development of a model for Mamdani type fuzzy rule-based systems using the new concept of granular computing (GrC) is presented. In this study a GrC algorithm is used to capture the required information in the form of data granules within a high dimensional complex database. The initial collection of information granules is used as a rule-base for a fuzzy inference system (FIS) which is optimised by utilising an Adaptive Genetic Algorithm (AGA). The proposed methodology is applied to real data relating to the heat treatment of alloy steels.
