Paper Info
Title
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
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.
Year
DOI
Venue
2011
10.1016/j.asoc.2010.09.004
Appl. Soft Comput.
Keywords
Field
DocType
fuzzy model,interpretability,fuzzy system,mamdani fuzzy model,modelling approach,confidence band,mechanical property,initial fuzzy model,hierarchical clustering,proposed modelling approach,particle swarm optimization,new training data selection,efficient data,multi-objective optimization,data selection,multi-objective optimisation mechanism,alloy steel,data-driven fuzzy modelling methodology,mamdani-type fuzzy modelling approach,steel,modeling,fuzzy,chemical composition,multi objective optimization,heat treatment
Data mining,Tolerance analysis,Multi-objective optimization,Artificial intelligence,Fuzzy control system,Hierarchical clustering,Particle swarm optimization,Interpretability,Mathematical optimization,Fuzzy logic,Confidence and prediction bands,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
11
2
Applied Soft Computing Journal
Citations 
PageRank 
References 
17
0.86
31
Authors
2
Name
Order
Citations
PageRank
Qian Zhang1384.41
Mahdi Mahfouf223533.17