A Quantitative Prediction Method of Network Security Situation Based on Wavelet Neural Network

2007 ◽  
Author(s):  
Lai Jibao ◽  
Wang Huiqiang ◽  
Liu Xiaowu ◽  
Liang Ying
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Quantitative Prediction

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

scholarly journals Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

2021 ◽  
Vol 2078 (1) ◽  
pp. 012067
Author(s):  
Jingcheng Zhao ◽  
Xiaomeng Li ◽  
Yaofu Cao ◽  
Junwen Liu ◽  
Junlu Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Security ◽  
Power Control ◽  
Situation Awareness ◽  
Wavelet Neural Network ◽  
Control Network ◽  
Industrial Control ◽  
True Value ◽  
Power Control System

Abstract In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

Health Assessment of Aircraft Based on Wavelet Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 4581-4585
Author(s):  
Chen Guang Zhao ◽  
Xu Ping Qi ◽  
Chen Ming Mi ◽  
Teng Fei Miao
Keyword(s):  
Neural Network ◽  
Health Assessment ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Fault Prediction ◽  
Health State ◽  
Learning Speed ◽  
Concept Of Health ◽  
Heavy Duty Gas Turbine ◽  
The Mathematical Model

Health assessment is one of the key technology in the aircraft operating system. Aiming at the characteristic of aircraft structure, the aircraft fault prediction method based on data mining is presented in this paper. The concept of health assessment is introduced first, the wavelet neural network provide the mathematical model reflecting aircraft health state. The experiment results show that the health prediction applying wavelet neural network works well with high fidelity and real time. Focusing at a typical heavy-duty gas turbine, the critical information collected by the sensor is applied as the network input, then the wavelet neural network is constructed, the quick training and learning speed is proved. The results indicate proposed approach is promising for reliable diagnostics of aircraft.

An Efficient Algorithm for Power Prediction in PV Generation System

2020 ◽  
Vol 9 (2) ◽  
pp. 207-216
Author(s):  
Qais Alsafasfeh
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Mean Values ◽  
Neural Network Algorithm ◽  
The Mean ◽  
Pv Generation ◽  
Error Indicators

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. ©2020. CBIORE-IJRED. All rights reserved

scholarly journals Network security situation prediction method based on strengthened LSTM neural network

2021 ◽  
Vol 1856 (1) ◽  
pp. 012056
Author(s):  
Zhaowei Dong ◽  
Xiaoyu Su ◽  
Lihui Sun ◽  
Kuikui Xu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Prediction Method

A Method of Network Security Situation Prediction Based on Gray Neural Network Model

2011 ◽  
Vol 63-64 ◽  
pp. 936-939 ◽  
Author(s):  
Nian Liu ◽  
Geng Li ◽  
Yong Liu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Prediction Method ◽  
Response Strategy ◽  
Network Information ◽  
Intelligent Analysis ◽  
Bp Neural Network Model

In this paper, a new network security situation intelligent analysis prediction method is proposed, which applies GM(1,1) model and BP neural network model in the analytic prediction field of network security situation information, and combination and optimization is performed to it to improve the accuracy of network security situation prediction. By analyzing and calculating the great amount of information acquired from network security situation evaluation system, it is able to make prediction on the current security situation of network system and the its future change trend, and make and implement relative response strategy according to prediction results, and reduce the harm from network attacks and improve the emergency response ability of network information system, so that we can make preparation before great damage occurs and reduce or avoid any possible attack to ensure the smooth running of system. The experiment results show that this method is a better solution for network security situation prediction.

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