A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

2000 ◽  
Vol 11 (6) ◽  
pp. 1251-1262 ◽  
Author(s):  
Jun Wang ◽  
Xue-Bin Liang
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Nonlinear Optimization ◽  
Recurrent Neural Network ◽  
Bound Constraints

An efficient grasshopper optimization with recurrent neural network controller-based induction motor to replace flywheel of the process machine

2020 ◽  
pp. 014233122093820
Author(s):  
Vasant M Jape ◽  
Hiralal M Suryawanshi ◽  
Jayant P Modak
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Induction Motor ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Power Electronic ◽  
Control Approach ◽  
Electronic Circuitry ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

This paper proposes a convenient power electronic circuitry with a control approach for the flywheel replacement of an induction motor. The proposed control approach is the joined execution of grasshopper optimization algorithm and recurrent neural network based on duty ratio controller and hence the proposed work is named grasshopper optimization with recurrent neural network. The main contribution of this work is, the power electronic circuitry gets the input voltage samples and limits the deviation to appraise the instantaneous torque demand. The required voltage for the instantaneous torque demand is produced by the proposed control technique. In the proposed grasshopper optimization with recurrent neural network technique, the grasshopper optimization algorithm is a meta-heuristic population-based algorithm, which works from the perspective of the swarming behavior of grasshoppers in nature. In the proposed system, the recurrent neural network learning procedure is improved by the grasshopper optimization algorithm in the perspective of the minimum error objective function. the proposed grasshopper optimization with recurrent neural network technique optimizes the inverter switching states by limiting the error between the setpoint torque and the demand torque regarding objective function. With this proposed technique, the unbalance between demand torque and generated torque is found with high precision and the quicker execution to pull back out the torsional pulsation insensitive load linked transmission systems. By utilizing the proposed methodology, the extreme fluctuation of load torque due to peaky loads in an induction motor will be detected accurately. Also, the proposed technique reduces the torsional vibrations, weakness in components and minimizes the outages of uninterrupted production leading to higher profits. The proposed strategy is actualized in the MATLAB/Simulink platform and evaluated their performance. The performances are appeared differently compared with the existing methods.

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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