Use of neural fuzzy networks with mixed genetic/gradient algorithmin automated vehicle control
Sunan Huang
Wei Ren
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec 1999
Volume: 46,
Issue: 6
On page(s): 1090-1102
ISSN: 0278-0046
References Cited: 28
CODEN: ITIED6
INSPEC Accession Number: 6443308
Digital Object Identifier: 10.1109/41.807993
Current Version Published: 2002-08-06
Abstract
This paper is concerned with the design of automated vehicle
guidance control. First, we propose to implement the guidance tasks
using several individual controllers. Next, a neural fuzzy network (NFN)
is used to build these controllers, where the NFN constructs are
neural-network-based connectionist models. A two-phase hybrid learning
algorithm which combines genetic and gradient algorithms is employed to
identify the NFN weightings. Finally, simulations are given to show that
the proposed technology can improve the speed of learning convergence
and enhance the performance of vehicle control
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