Creation of Fuzzy Control Table with Data Mining

Jun Gao

doi:10.4028/www.scientific.net/AMR.760-762.1080

Paper Titles

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System
p.1062

Development of the Online Automatic Measuring System for Total Sugar Content in Chlortetracycline Fermenter
p.1067

Research and Application of Automatic Measuring System in Fermenters Total Sugar Content
p.1071

Research of Several PID Algorithms Based on MATLAB
p.1075

Creation of Fuzzy Control Table with Data Mining
p.1080

Research on Software Testability Requirement Analysis Method
p.1084

Research of AMU Unit Fault Testability Based on Multi-Signal Modeling and Fuzzy Decision
p.1089

Research of Reader Network Access Management in IOT
p.1095

Trusted Class Measurement Module - A Novel Fine-Grained Integrity Measurement Method for Java Application
p.1100

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Creation of Fuzzy Control Table with Data Mining

Creation of Fuzzy Control Table with Data Mining

Abstract:

A good fuzzy control table is the key to a fuzzy control system, and the systems performance mainly depends on the quality of the table. Based on analyzing fully the principles of a typical fuzzy control systems and the procedures of building a fuzzy control table, this paper presents a new method of applying the boolean association rule data mining techniques to mining of fuzzy control table directly from the database of manual operating records.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1080-1083

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.1080

Citation:

Cite this paper

Online since:

September 2013

Authors:

Jun Gao

Keywords:

Association Rule, Data Mining (DM), Fuzzy Control, Fuzzy Control Table

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Jiawei Han, "Data Mining: Concepts and Techniques. Burnaby: Simon Fraser University, 2009. 156-16.

Google Scholar

[2] Qing Wei, Guoqing Chen. Mining generalized association rules with fuzzy taxonomic structure, NAFIPS99 (2009).

Google Scholar

[3] Hellerstein J M, Avnur R., teractive data analysis: The control project, IEEE Computer, 2010, 32(7): 51-59.

Google Scholar

[4] Han J, Pei J. , Mining frequent patterns without candidate generation, n Proceedings of 2010 ACM SIGMOD International Conference on Management of Data. Dallas, 2000. 1-12.

DOI: 10.1145/342009.335372

Google Scholar

[5] Pasquier N, Bastide Y, fficient mining of association rules using closed itemset lattices, Information Systems, 2009, 24(1): 25-46.

DOI: 10.1016/s0306-4379(99)00003-4

Google Scholar