A new MEBML-based algorithm for adjusting parameters online

Author

Xie, Keming ; Mou, Changhua ; Xie, Gang ; Sun, Chenyi

Author_Institution

Coll. of Inf. Eng., Taiyuan Univ. of Technol., China

Volume

3

fYear

2000

fDate

2000

Firstpage

1714

Abstract

An MEBML (mind-evolution-based machine learning)-based algorithm for adjusting parameters online is proposed. This new AI algorithm can provide laws for parameters adjusted online by adopting the new learning system-MEBML and building the adjusting functions. This new algorithm is applied in adjusting the output scaling factor of the fuzzy logic controller (FLC). In this way, a new FLC is constructed. Conclusions can be drawn from simulation results: 1) MEBML has the rapid convergence rate and high calculation accuracy; 2) the new algorithm is easy and effective; 3) the performance of the new FLC is good

Keywords

fuzzy control; fuzzy set theory; intelligent control; learning (artificial intelligence); MEBML-based algorithm; fuzzy logic controller; high calculation accuracy; mind-evolution-based machine learning-based algorithm; output scaling factor; rapid convergence rate; Control systems; Fuzzy control; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Humans; Learning systems; Machine learning algorithms; Scattering; Sun;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.893432

Filename

893432