Please use this identifier to cite or link to this item: https://doi.org/10.1109/41.807993
Title: Use of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control
Authors: Huang, S. 
Ren, W.
Issue Date: 1999
Source: Huang, S., Ren, W. (1999). Use of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control. IEEE Transactions on Industrial Electronics 46 (6) : 1090-1102. ScholarBank@NUS Repository. https://doi.org/10.1109/41.807993
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.
Source Title: IEEE Transactions on Industrial Electronics
URI: http://scholarbank.nus.edu.sg/handle/10635/62917
ISSN: 02780046
DOI: 10.1109/41.807993
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