| Sun Yushan（孙玉山）,Wang Jianguo,Wan Lei,Hu Yunyan,Jiang Chunmeng.[J].高技术通讯(英文),2012,18(3):243~247 |
|
| Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm |
| Revised:October 10, 2026 |
| DOI：10.3772/j.issn.1006-6748.2012.03.005 |
| 中文关键词: |
| 英文关键词: underwater vehicle (UV), system identification, neural network, genetic algorithm (GA), back propagation algorithm |
| 基金项目: |
| Author Name | Affiliation | | Sun Yushan（孙玉山） | | | Wang Jianguo | | | Wan Lei | | | Hu Yunyan | | | Jiang Chunmeng | |
|
| Hits: 1199 |
| Download times: 1337 |
| 中文摘要: |
| |
| 英文摘要: |
| Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm (GA) and error back propagation algorithm (BP) is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle (UV)’s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification precision. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
