Referenced Items are not available for this document.
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
Published in:
Industrial Electronics, IEEE Transactions on
(Volume:46
,
Issue:
6
)
Date of Publication: Dec 1999
- Page(s):
- 1090 - 1102
- ISSN :
- 0278-0046
- INSPEC Accession Number:
- 6443308
- Digital Object Identifier :
- 10.1109/41.807993
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Dec 1999
- Sponsored by :
- IEEE Industrial Electronics Society
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