The Application of an Annealing Recurrent Neural Network for Extremum Seeking Algorithm to Optimize UAV Tight Formation Flight

2006 ◽  
Author(s):  
Yun-an Hu ◽  
Bin Zuo ◽  
Xiaodong Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Formation Flight ◽  
Extremum Seeking ◽  
Tight Formation

scholarly journals Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Boyang Zhang ◽  
Xiuxia Sun ◽  
Shuguang Liu ◽  
Xiongfeng Deng
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Recurrent Neural Network ◽  
Optimization Problem ◽  
Hierarchical Control ◽  
Formation Flight ◽  
De Algorithm ◽  
Formation Tracking ◽  
Adaptive Differential Evolution

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

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.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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