Recurrent Neural Network Identification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes

10.5772/5554 ◽  
2008 ◽  
Author(s):  
Ieroham Baruch ◽  
Carlos Mariaca-Gaspar ◽  
Josefina Barrera-Cortes
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Control ◽  
Hydrocarbon Biodegradation ◽  
Adaptive Neural Control ◽  
Network Identification

scholarly journals Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Heidari ◽  
Hadi Homaei
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Control ◽  
Closed Loop ◽  
Control Valve ◽  
Superior Performance ◽  
Pneumatic Control ◽  
Response Characteristics ◽  
Control Scheme ◽  
Simulation Results

This paper presents a neural scheme for controlling an actuator of pneumatic control valve system. Bondgraph method has been used to model the actuator of control valve, in order to compare the response characteristics of valve. The proposed controller is such that the system is always operating in a closed loop, which should lead to better performance characteristics. For comparison, minimum- and full-order observer controllers are also utilized to control the actuator of pneumatic control valve. Simulation results give superior performance of the proposed neural control scheme.

scholarly journals Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jahanzeb Rajput ◽  
Jingping Shi ◽  
Wen Ma
Keyword(s):  
Neural Network ◽  
Neural Control ◽  
Tracking Error ◽  
Input Saturation ◽  
Small Neighborhood ◽  
High Order ◽  
Inversion Method ◽  
Adaptive Neural Control ◽  
Morphing Aircraft ◽  
Longitudinal Dynamics

This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.

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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