Paper Info
Title
Evolving fuzzy model based performance identification for production control
Abstract
In this paper we present a fuzzy cloud-based model identification method tested on realistic input/output data signals acquired from simulated Tennessee Eastman (TE) benchmark process. The cloud-based method uses simplified antecedent (IF) part based on the local density of the clouds and functional consequent (THEN) part. Number of clouds (fuzzy rules) in the IF part evolves such that when certain criteria are satisfied a new cloud is added. In this paper we use simple density threshold complemented with protecting mechanism for outliers. The parameters of the consequent part were identified using recursive Weight Least Square method. The proposed method was tested on TE process where three models were identified for the chosen, most representative, production Performance Indicators (pPIs). The provided results (quality measures) of the proposed method were compared with the results obtained using eFuMo identification tool.
Year
DOI
Venue
2016
10.1109/EAIS.2016.7502496
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Keywords
Field
DocType
evolving fuzzy model based performance identification,production control,fuzzy cloud-based model identification method tested,realistic input-output data signals,Tennessee Eastman benchmark process,TE benchmark process,simplified antecedent part,cloud and functional consequent part,density threshold,outlier protecting mechanism,weight least square method,production performance indicators,pPIs,eFuMo identification tool,fuzzy rule-based system
Least squares,Data mining,Data modeling,Performance indicator,Production control,Fuzzy logic,Outlier,Fuzzy control system,Engineering,System identification
Conference
ISSN
Citations 
PageRank 
2330-4863
1
0.37
References 
Authors
19
4
Name
Order
Citations
PageRank
Goran Andonovski1283.97
Gasper Music2679.04
Saso Blazic315129.21
Igor Skrjanc435452.47