Effectiveness Evaluation of Communication Network based on BP neural network

2021 ◽  
Author(s):  
Chi Han ◽  
Wei Xiong ◽  
Ping Jian
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Bp Neural Network ◽  
Effectiveness Evaluation

Smith Predictor Based on Predicting Induced-Delay

2011 ◽  
Vol 201-203 ◽  
pp. 2003-2006
Author(s):  
Shu De Li ◽  
Yi Chen ◽  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Control System ◽  
Bp Neural Network ◽  
Networked Control System ◽  
Smith Predictor ◽  
Networked Control ◽  
Object Model ◽  
Random Delay

Since communication network is introduced into control system, induced-delay appears. Because of the delay, the performance of networked control system becomes bad, even unsteady. Conventional Smith predictor is sensitive to error in object model and needs delay’s value in advance. Regarding random delay, its application is limited. In this paper, we propose a method based on induced-delay predicted by BP neural network, which use two historical delay values to predict the next one. Smith predictor adjusts its parameters according to that value in time. The simulating results indicate that the precision of delay-predicting can be ensured and the performance of networked control system has been improved.

Study on the Effectiveness Evaluation of the Weapon Equipment Based on Artificial Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3262-3265
Author(s):  
Feng Gao ◽  
Fei Song ◽  
Guo Qing Huang ◽  
Mao Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Effectiveness Evaluation ◽  
Matlab Toolbox ◽  
New Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Better Than

A new approach to weapons and equipment effectiveness evaluation based on artificial neural network (ANN) performs better than traditional method, which is in view of the complex relationship between the effectiveness and many factors that influence the evaluation. The structure and learning algorithm of BP neural network is evaluated the fighters’ air-to-air combat capability. The evaluation is accomplished by a two-layer BP neural network and MATLAB toolbox. The simulation results show that the artificial neural network have better generalization ability and approximation performance for continuous function, which is valuable in weapons and equipment effectiveness evaluation application.

scholarly journals Risk Assessment of Power Communication Network Based on LM-BP Neural Network

2019 ◽  
Vol 1187 (2) ◽  
pp. 022063
Author(s):  
Yanan Wang ◽  
Ke Wang ◽  
Ran Zhang ◽  
Qiao Xue ◽  
Xiangzhou Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Communication Network ◽  
Bp Neural Network

A Wavelet Kernel-Based Support Vector Machine for Communication Network Intrusion Detection

2014 ◽  
Vol 989-994 ◽  
pp. 4474-4477
Author(s):  
Ying Zhan
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Better Than

This study is to propose a wavelet kernel-based support vector machine (SVM) for communication network intrusion detection. The common intrusion types of communication network mainly include DOS, R2L, U2R and Probing. SVM, BP neural network are used to compare with the proposed wavelet kernel-based SVM method to show the superiority of wavelet kernel-based SVM. The detection accuracy for communication network intrusion of wavelet kernel-based SVM is 96.67 %, the detection accuracy for communication network intrusion of SVM is 90.83%, and the detection accuracy for communication network intrusion of BP neural network is 86.67%.It can be seen that the detection accuracy for communication network intrusion of wavelet kernel-based SVM is better than that of SVM or BP neural network.

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