scholarly journals Recurrent Neural Network for State Adjustment of Redundant Manipulators

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109783-109790
Author(s):  
Long Jin ◽  
Li He ◽  
Zhiguan Huang ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Redundant Manipulators

Second Order Recurrent Neural Network for the Inverse Kinematics of Redundant Manipulators

2013 ◽  
Vol 391 ◽  
pp. 114-117
Author(s):  
Run Sheng Hao
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inverse Kinematics ◽  
Second Order ◽  
Degree Of Freedom ◽  
Redundant Manipulators ◽  
Good Precision ◽  
Inverse Kinematics Problem ◽  
Simulation Results ◽  
Three Degree Of Freedom

In this paper, the second order recurrent neural network is adopted to study the inverse kinematics problem of three degree-of-freedom planar redundant manipulators. The Simulation results show that the network can effectively solve the inverse kinematics problem of redundant manipulators, and it reaches to good precision of solution and solving speed.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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