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Input weighted data granulation using hybrid correlation measures with application to metal properties

Panoutsos, George, Mahfouf, Mahdi, Zhang, Qian, Gaffour, Sidahmed (2009) Input weighted data granulation using hybrid correlation measures with application to metal properties. In: Automation in Mining, Mineral and Metal Processing. 1 (1). pp. 272-277. (doi:10.3182/20091014-3-CL-4011.00049) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://doi.org/10.3182/20091014-3-CL-4011.00049

Abstract

This paper introduces a new data granulation algorithm using significance weights on the input space of the data set. This data granulation algorithm aims to provide a more reliable way of grouping data together by directing the data granulation to favor the most significant variables of the process under investigation. Such a data granulation algorithm assists in the elicitation of the initial rule-base of a fuzzy or neural-fuzzy model. A hybrid correlation index, called Significance Index, is introduced to rank the process variables based on the linear correlation coefficient and the partial correlation measure. The new algorithm is used to classify the process variables and subsequently model and predict mechanical properties of heat treated steel. The property under investigation is the Tensile Strength and the case study data set consists of chemical composition and microstructure measurements coupled with Tensile Strength measurements.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.3182/20091014-3-CL-4011.00049
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: 18 Sep 2015 16:20 UTC
Last Modified: 29 May 2019 16:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50551 (The current URI for this page, for reference purposes)
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