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Efficient neuro-fuzzy control systems for autonomous underwater vehicle control

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2 Author(s)
Jeen-Shing Wang ; Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA ; C. S. G. Lee

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.

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Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on  (Volume:3 )

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