Title :
Unmanned hybrid electric vehicles FNN control based on self-organized learning algorithm and supervised learning algorithm
Author :
Zhang, Yun ; Yu, Xiumin ; Chen, Xuemei ; Bi, Mingshuang
Author_Institution :
Coll. of Automobile Eng., Jilin Univ., Changchun, China
Abstract :
To resolve unmanned hybrid electric vehicles control problems, the fuzzy neural network control method based on self-organized learning algorithm and supervised learning algorithm is proposed in this paper. This algorithm can learn proper fuzzy logic rules and optimal memberships functions from training examples. Using this control method,,we can control an unmanned hybrid electric vehicle by learning the driving technique of a skilled drive. By combining both unsupervised self-organized and supervised learning algorithm,the learning speed converges much faster than the original backpropagation learning algorithm. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm.
Keywords :
backpropagation; fuzzy neural nets; fuzzy set theory; hybrid electric vehicles; learning systems; neurocontrollers; optimal control; remotely operated vehicles; self-adjusting systems; FNN control; backpropagation learning algorithm; driving technique learning; fuzzy logic rules; fuzzy neural network control; optimal membership function; self-organized learning algorithm; skilled drive; supervised learning algorithm; unmanned hybrid electric vehicles; Artificial intelligence; Artificial neural networks; Computational modeling; Niobium; Robots; Vehicles; fuzzy neural network(FNN); self-organized learning algorithm; supervised learning algorithm; unmanned hybrid electric vehicle;
Conference_Titel :
Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-7957-3
DOI :
10.1109/CMCE.2010.5610320