TY - GEN
T1 - Efficient neuro-fuzzy control systems for autonomous underwater vehicle control
AU - Wang, J. S.
AU - Lee, C. S.G.
PY - 2001
Y1 - 2001
N2 - This paper examines several clustering methods for the 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.
AB - This paper examines several clustering methods for the 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.
UR - https://www.scopus.com/pages/publications/0034868489
UR - https://www.scopus.com/pages/publications/0034868489#tab=citedBy
U2 - 10.1109/ROBOT.2001.933075
DO - 10.1109/ROBOT.2001.933075
M3 - Conference contribution
AN - SCOPUS:0034868489
SN - 0780365763
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2986
EP - 2991
BT - Proceedings - IEEE International Conference on Robotics and Automation
T2 - 2001 IEEE International Conference on Robotics and Automation
Y2 - 21 May 2001 through 26 May 2001
ER -