Skip to main content

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

Zhang, Qian, Mahfouf, Mahdi (2011) A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels. Applied Soft Computing, 11 (2). pp. 2419-2443. ISSN 1568-4946. (doi:10.1016/j.asoc.2010.09.004)

Abstract

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2010.09.004
Uncontrolled keywords: Fuzzy; Modeling; Interpretability; Hierarchical clustering; Data selection; Multi-objective optimization; Particle swarm optimization; Confidence band; Mechanical property, Steel
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 17 Sep 2015 16:55 UTC
Last Modified: 29 May 2019 16:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50507 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year