Efficient neuro-fuzzy control systems for autonomous underwater vehicle control

Author

Wang, Jeen-Shing ; Lee, C. S George

Author_Institution

Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

3

fYear

2001

fDate

2001

Firstpage

2986

Abstract

Examines several clustering methods for structure learning in constructing efficient neuro-fuzzy systems. The structure learning establishes the internal structure (i.e., the number of term sets and fuzzy-rule base generation) of a given neuro-fuzzy architecture. The fundamental ideas of existing rule generation algorithms are addressed and discussed. Performance of the neuro-fuzzy systems established from these clustering methods is validated through computer simulations of the classification problem of IRIS and the control example of an autonomous underwater vehicle.

Keywords

feedforward neural nets; fuzzy control; fuzzy systems; knowledge acquisition; learning (artificial intelligence); mobile robots; multilayer perceptrons; neurocontrollers; pattern clustering; underwater vehicles; IRIS; autonomous underwater vehicle control; classification problem; clustering methods; fuzzy-rule base generation; internal structure; neuro-fuzzy control systems; structure learning; term sets; Clustering algorithms; Clustering methods; Control systems; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Iris; Mobile robots; Remotely operated vehicles; Underwater vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on

ISSN

1050-4729

Print_ISBN

0-7803-6576-3

Type

conf

DOI

10.1109/ROBOT.2001.933075

Filename

933075