Research on Intrusion Detection System Based on BP Neural Network

Wei, Mingjun; Su, Jing-Jing; Jin, Jianguo; Wang, Le

doi:10.1007/978-3-642-40618-8_85

Cited by 3 publications

(2 citation statements)

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“…Researchers have proposed many methods on the control algorithm, including fuzzy control, [12][13][14][15][16][17] sliding mode control, [18][19][20] neural networks, [21][22][23][24][25][26] iterative learning, [27][28][29][30][31] etc.…”

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confidence: 99%

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Tension control of a yarn winding system based on the nonlinear active disturbance-rejection control algorithm

Zhang

Xia

Lu³

et al. 2022

Textile Research Journal

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This research proposes a new system for controlling yarn tension in the winding operation based on active disturbance-rejection control (ADRC) technology, which controls and minimizes the fluctuation of tension during winding. The mechanical structure of the winding machine was analyzed to clarify the main factors affecting the yarn tension. Proportional–integral–derivative (PID) control is the most common control method of the winding machine. In order to compare the control effects of ADRC and PID control, the mathematical model of the yarn-tension control system was established on the MATLAB platform by means of system identification. Through the simulation on this model, the results showed that under the action of disturbance, the overshoot of the yarn tension controlled by the active disturbance-rejection controller was smaller, the adjustment time was shorter, and the steady-state error was smaller. An experimental platform was built to test the control effect of the controller under different parameters, thereby verifying the performance and stability of the active disturbance-rejection controller. The results showed that the controller had good performance and stability.

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confidence: 99%

“…This application provides a suitable method for spinning tension prediction, which is of great significance to the tension control of chemical fiber products. Researchers have proposed many methods on the control algorithm, including fuzzy control, 12–17 sliding mode control, 18–20 neural networks, 21–26 iterative learning, 27–31 etc.…”

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confidence: 99%

Tension control of a yarn winding system based on the nonlinear active disturbance-rejection control algorithm

Zhang

Xia

Lu³

et al. 2022

Textile Research Journal

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Trends in Application of Machine Learning to Network-Based Intrusion Detection Systems

Hrabovsky

Segeč

Moravčík

et al. 2018

Communications in Computer and Information Science

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Research on Intrusion Detection Based on Deep Confidence Network

余¹

2018

CSA

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As an active security prevention technology, intrusion detection has been used in network security for a long time. But with the development and application of Internet, network attack and intrusion are constantly changing in quantity and technology level. Based on new types and concurrent attacks, traditional intrusion detection technology has been unable to meet the requirements of existing network security. As a frontier technology of machine learning and artificial intelligence, deep learning has made great achievements in speech recognition, computer vision and big data processing, and has also provided a new idea for solving the current intrusion detection problem. This paper studies the traditional intrusion detection technology based on learning method combined with the depth and the depth of the belief network, proposes an intrusion detection technology based on deep belief networks, according to the characteristics of intrusion detection data of over sampling and non [0, 1] interval of data normalization, updates the parameters in the deep belief network in the process of using variable number of the gradient descent algorithm to speed up the learning rate, the parameters of the update process, and in each batch of the training data, join the discrimination on the labels, and improve the accuracy. Experiments show that the accuracy of intrusion detection can be improved greatly by using the method proposed in this paper.

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Research on Intrusion Detection System Based on BP Neural Network

Cited by 3 publications

References 0 publications

Tension control of a yarn winding system based on the nonlinear active disturbance-rejection control algorithm

Tension control of a yarn winding system based on the nonlinear active disturbance-rejection control algorithm

Trends in Application of Machine Learning to Network-Based Intrusion Detection Systems

Research on Intrusion Detection Based on Deep Confidence Network

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