Rule Extraction From Fuzzy-Based Blast Furnace SVM Multiclassifier for Decision-Making

2014 ◽  
Vol 22 (3) ◽  
pp. 586-596 ◽  
Author(s):  
Chuanhou Gao ◽  
Qinghuan Ge ◽  
Ling Jian
Keyword(s):  
Decision Making ◽  
Blast Furnace ◽  
Rule Extraction

Stable rule extraction and decision making in rough non-deterministic information analysis

2011 ◽  
Vol 8 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Hiroshi Sakai ◽  
Hitomi Okuma ◽  
Michinori Nakata ◽  
Dominik Ślȩzak
Keyword(s):  
Decision Making ◽  
Rule Extraction ◽  
Information Analysis

Software for decision-making support in blast-furnace operation

2017 ◽  
Vol 47 (8) ◽  
pp. 538-543 ◽  
Author(s):  
V. V. Lavrov ◽  
N. A. Spirin ◽  
I. A. Gurin ◽  
V. Yu. Rybolovlev ◽  
A. V. Krasnobaev
Keyword(s):  
Decision Making ◽  
Blast Furnace ◽  
Furnace Operation ◽  
Blast Furnace Operation ◽  
Decision Making Support

Two-Stage Decision-Making Method for Burden Distribution Based on Recognition of Conditions in Blast Furnace

2020 ◽  
pp. 1-1
Author(s):  
Yuanfeng Huang ◽  
Xuzhi Lai ◽  
Kexin Zhang ◽  
Jianqi An ◽  
Lue-Feng Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Blast Furnace ◽  
Two Stage ◽  
Burden Distribution

scholarly journals An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3535 ◽  
Author(s):  
Wang ◽  
Li ◽  
Zhang ◽  
Zou
Keyword(s):  
Decision Making ◽  
Liquid Steel ◽  
Classification Problem ◽  
Rule Extraction ◽  
Black Box ◽  
Production Data ◽  
Integrated Methodology ◽  
Irrelevant Attributes ◽  
Learning Machine ◽  
Elm Classifier

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

Decision-Making Support in Blast-Furnace Operation

2019 ◽  
Vol 49 (3) ◽  
pp. 185-193
Author(s):  
A. V. Pavlov ◽  
A. A. Polinov ◽  
N. A. Spirin ◽  
O. P. Onorin ◽  
V. V. Lavrov ◽  
...  
Keyword(s):  
Decision Making ◽  
Blast Furnace ◽  
Furnace Operation ◽  
Blast Furnace Operation ◽  
Decision Making Support

Research on Rough Set Theory-Based Customer Division

2014 ◽  
Vol 989-994 ◽  
pp. 1536-1540
Author(s):  
Gui Juan Song ◽  
Xin Cao ◽  
Xin Yue Wang
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Rules ◽  
Main Idea ◽  
Rule Extraction

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. This paper presents the design of model for customer division based on rough set, and uses algorithms for attribute reduction and rule extraction in rough set to analyze the customer of supermarket. This paper also introduces how to achieve the minimum result of attribute reduction and decision-making via decision-making report, winkling redundant attribute and over-rule of decision.

scholarly journals UNCERTAINTY HANDLING IN DISASTER MANAGEMENT USING HIERARCHICAL ROUGH SET GRANULATION

2015 ◽  
Vol II-3/W5 ◽  
pp. 271-276 ◽  
Author(s):  
H. Sheikhian ◽  
M. R. Delavar ◽  
A. Stein
Keyword(s):  
Decision Making ◽  
Granular Computing ◽  
Seismic Vulnerability ◽  
Quality Measures ◽  
Rule Extraction ◽  
Training Data ◽  
Classification Rules ◽  
Earthquake Intensity ◽  
Data Set ◽  
The North

Uncertainty is one of the main concerns in geospatial data analysis. It affects different parts of decision making based on such data. In this paper, a new methodology to handle uncertainty for multi-criteria decision making problems is proposed. It integrates hierarchical rough granulation and rule extraction to build an accurate classifier. Rough granulation provides information granules with a detailed quality assessment. The granules are the basis for the rule extraction in granular computing, which applies quality measures on the rules to obtain the best set of classification rules. The proposed methodology is applied to assess seismic physical vulnerability in Tehran. Six effective criteria reflecting building age, height and material, topographic slope and earthquake intensity of the North Tehran fault have been tested. The criteria were discretized and the data set was granulated using a hierarchical rough method, where the best describing granules are determined according to the quality measures. The granules are fed into the granular computing algorithm resulting in classification rules that provide the highest prediction quality. This detailed uncertainty management resulted in 84% accuracy in prediction in a training data set. It was applied next to the whole study area to obtain the seismic vulnerability map of Tehran. A sensitivity analysis proved that earthquake intensity is the most effective criterion in the seismic vulnerability assessment of Tehran.

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