Trajectory Tracking of Complex Dynamical Network for Recurrent Neural Network Via Control V-Stability

2009 ◽  
Author(s):  
José P. Pérez ◽  
Joel Pérez ◽  
Jorge A. González
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Trajectory Tracking ◽  
Complex Dynamical Network ◽  
Dynamical Network

scholarly journals Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jose P. Perez ◽  
Joel Perez Padron ◽  
Angel Flores Hemandez ◽  
Santiago Arroyo
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Trajectory Tracking ◽  
Recurrent Neural Networks ◽  
Tracking Error ◽  
Network Control ◽  
Lyapunov Theory ◽  
Control Law ◽  
Complex Dynamical Network ◽  
Dynamical Network

In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

PD Control Law for Trajectory Tracking of Chaotic Pendulum

2013 ◽  
Vol 646 ◽  
pp. 208-215
Author(s):  
Joel P. Perez ◽  
Jose P. Perez ◽  
Francisco Rdz ◽  
Angel H. Flores
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Trajectory Tracking ◽  
Experimental Results ◽  
Control Law ◽  
Adaptive Neural Network ◽  
Pd Control ◽  
Dynamical Network ◽  
Analytical Results

This paper presents the application of trajectory tracking using the adaptive neural network to the double chaotic pendulum. The proposed controller structute is composed of a neural identifier and a PD Control. Experimental results with the chaotic pendulun shown the usefulness of the proposed approach. To verify the analytical results, an example of dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system.

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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