Redundant manipulator infinity-norm joint torque optimization with actuator constraints using a recurrent neural network

2002 ◽  
Author(s):  
Wai Sum Tang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Joint Torque ◽  
Redundant Manipulator ◽  
Infinity Norm ◽  
Actuator Constraints

Multifunctional Electrical Prosthetic Hand -Development of Tendon-driven Mechanism and Controller

2002 ◽  
Vol 14 (6) ◽  
pp. 557-564 ◽  
Author(s):  
Wenwei Yu ◽  
◽  
Daisuke Nishikawa ◽  
Yasuhiro Ishikawa ◽  
Hiroshi Yokoi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Joint Torque ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
Nonlinear Properties ◽  
The Stability

The purpose of this research was to develop a tendondriven electrical prosthetic hand, which is characterized by its mechanical torque-velocity converter and a mechanism that can assist proximal joint torque by distal actuators. To cope with time-delay and nonlinear properties of the prosthetic hand, a controller based on a Jordan network, recurrent neural network models, is proposed. The results of experiments on the stability of the controller are confirmed when tracking static wire tensions. Finally, the next prototype of prosthetic hand based on these methods is introduced.

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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