Identification of Non-linear Dynamic Model of UUV Based on ESN Neural Network
Unmanned underwater vehicle (UUV) is a highly complex nonlinear dynamic system, and neural network has the ability to arbitrary approximate nonlinear system in theoretically. Furthermore, echo state network (ESN) is a new type recurrent neural network based on state reservoir. To improve the accuracy of UUVs dynamic model, this paper based on the use of echo state networks (ESN) of the system identification method, using meta-learning strategy for offline training ESN network and genetic algorithm to optimize the main parameters, to remove the difficulty of choosing the ESN parameters. This method was applied to approximate of dynamic model of six degree of freedom of UUV, and build on the dynamic model. Finally, the simulation proved that the network structure identification algorithm has a good approximation ability and fast training speed.
BIAN Xinqian MOU Chunhui
Harbin Engineering University, Harbin 150001, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）