Network Security Based on D-S Evidence Theory Optimizing CS-BP Neural Network Situation Assessment

2018 ◽  
Author(s):  
Jie Qiang ◽  
Fei Wang ◽  
Xin-lai Dang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Situation Assessment

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Study on Network Security Based on PCA and BP Neural Network Under Green Communication

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 53733-53749 ◽  
Author(s):  
Fengchun Liu ◽  
Wenjie Huo ◽  
Yang Han ◽  
Shichao Yang ◽  
Xiaoyu Li
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Green Communication

Multisensor Information Fusion Based on D-S Evidence Theory and BP Neural Network

2013 ◽  
Vol 567 ◽  
pp. 113-117 ◽  
Author(s):  
Can Zhao ◽  
C.R. Tang ◽  
S. Wan
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Evidence Theory ◽  
Vibration Signal ◽  
Inspection System ◽  
Tool Monitoring

This paper applies the information fusion technology to tool monitoring. As one of the most important processing factor, the cutting tool and the tool wear directly influence size precision. Signals of cutting force and vibration are measured with multi-sensor. By using multi-sensor the drawbacks can be overcome, the multi-sensor information fusion mentioned in manufacture stands for extracting kinds of information from different sensors (especially for cutting force and vibration signal in this paper), making best use of all resources，according to certain criterion to judge the spatial and time redundancy , to make the system more comprehensive. Two data fusion methods, which are BP Neural Network and Wavelet Neural Network for predicting tool wear, and are debated. By the hybrid of BP and wavelet based neural network the cutting tool status inspection system is built so that the forecast of tool wear can be achieved. The results show experimentally two of these presented methods effectively implement tool wear monitoring and predicting.

Vibration Fault Diagnosis for Hydroelectric Generating Units Based on the Modified Cuckoo Search Algorithm and Evidence Theory

2019 ◽  
Vol 13 (3) ◽  
pp. 281-288
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Yan Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Evidence Theory ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
System Structure ◽  
Hybrid Scheme ◽  
Independent Evidence

Background: In view of the complex system structure and uncertain factors in the fault diagnosis of hydroelectric generating units (HGU), it is a difficult problem to design the diagnosis method rationally. Objective: An attempt is made to employ multi-source feature information to improve the accuracy of fault diagnosis, and the effectiveness of the proposed scheme is verified by using a diagnostic example. Methods: Through the research on recent papers and patents related to fault diagnosis of the HGU, a hybrid scheme based on the modified cuckoo search algorithm, back-propagation (BP) neural network and evidence theory are proposed. For this modified version named cuckoo search with fitness information (CSF), the step factor is adaptively tuned using the fitness value. Next, three diagnostic models based on BP neural network trained by CSF are used for primary diagnosis. These diagnostic results are then used as the independent evidence, and the fusion decision is made by using evidence theory. Results: Experimental results show that CSF algorithm is better than the original cuckoo search (CS) and its three variants, and the hybrid method has the highest diagnostic accuracy. Conclusion: The proposed hybrid scheme has strong robustness and fault tolerance, and can effectively classify the vibration faults of hydroelectric generating units

The Application of BP Neural Network in Diagnosing the Short Circuit Fault of Winding

2014 ◽  
Vol 543-547 ◽  
pp. 1064-1067
Author(s):  
Jian Qun Zhang ◽  
De Jian Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Evidence Theory ◽  
Accurate Diagnosis ◽  
Bp Neural Networks ◽  
Rotor Winding ◽  
Short Circuit Fault

As a common fault of motor, the short circuit of rotor winding is important for the accurate diagnosis. In this article, the author collected every status parameter of motor by different sensors, using two BP neural networks to partly diagnose the motor and fusing the results of partly diagnosis by D-S evidence theory. The author increases the creditability of diagnosis results by practices and decreases uncertainty, showing the efficiency of this method.

scholarly journals An efficient method for network security situation assessment

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Xiaoling Tao ◽  
Kaichuan Kong ◽  
Feng Zhao ◽  
Siyan Cheng ◽  
Sufang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Data Storage ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Method ◽  
Trend Prediction ◽  
Situation Assessment ◽  
Situational Assessment

Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document