Motion model identification of rescue robot based on optimized Jordan neural network

被引：0

作者：

Zhang, Guangbin ^{[1
]}

Zhang, Runmei ^{[2
]}

Wang, Guangyin ^{[1
]}

Wu, Yulu ^{[1
]}

机构：

[1] Anhui Jianzhu Univ, Sch Elect & Informat Engn, Hefei 230601, Anhui, Peoples R China

[2] Anhui Jianzhu Univ, Sch Mech & Elect Engn, Hefei 230601, Anhui, Peoples R China

来源：

3RD INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY, ENVIRONMENT AND CHEMICAL ENGINEERING | 2017年 / 69卷

关键词：

PREDICTION; HULL;

D O I：

10.1088/1755-1315/69/1/012189

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Considering the influence of various factors, such as speed, angle, depth of water, weight, and water flow, on the underwater rescue robot, a method based on neural network is proposed. According to the characteristics of Elman and Jordan neural network, a new dynamic neural network is constructed. The network can be used to remember the state of the hidden layer and increase the feedback of the output node. The improved Jordan network is optimized by chaos particle swarm optimization algorithm. The optimized neural network is applied to identify the dynamic model of the underwater rescue robot. The simulation results show that the neural network has good convergence speed and accuracy.

引用

页数：7

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