scholarly journals A network security situation prediction model based on wavelet neural network with optimized parameters

2016 ◽  
Vol 2 (3) ◽  
pp. 139-144 ◽  
Author(s):  
Haibo Zhang ◽  
Qing Huang ◽  
Fangwei Li ◽  
Jiang Zhu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Prediction Model ◽  
Wavelet Neural Network ◽  
Model Based

The Study of Prediction Model Based on Morlet Wavelet Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 4847-4851 ◽  
Author(s):  
Hui Zhen Yang ◽  
Wen Guang Zhao ◽  
Wei Chen ◽  
Xu Quan Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Algorithm ◽  
Morlet Wavelet ◽  
Wavelet Neural Network ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Wavelet Neural Network (WNN) is a new form of neural network combined with the wavelet theory and artificial neural network. The wavelet neural network model based on Morlet wavelet and the corresponding learning algorithm were studied in this paper. And through learning the wavelet neural network model is applied to all kinds of engineering examples, it proved that the wavelet neural network prediction model which has a more flexible and efficient function approximation ability and strong fault tolerance, and with high predicting precision.

scholarly journals Hot Spot Data Prediction Model Based on Wavelet Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Zhang ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Storage Systems ◽  
Hot Spot ◽  
Hard Disk ◽  
Wavelet Neural Network ◽  
Data Block ◽  
Data Migration ◽  
Storage Device ◽  
Model Based

The novel hybrid multilevel storage system will be popular with SSD being integrated into traditional storage systems. To improve the performance of data migration between solid-state hard disk and hard disk according to the characteristics of each storage device, identifying the hot data block is significant issue. The hot data block prediction model based on wavelet neural network is built and trained by using historical data. This prediction model can overcome the cumulative effect of traditional statistical methods and has strong sensitivity to I/O loads with random variations. The experimental results show that the proposed model has better accuracy and faster learning speed than BP neural network model. In addition, it has less dependence on sample data and has better generalization ability and robustness. This model can be applied to the data migration of distributed hybrid storage systems to improve performance.

Wind Power Prediction Model Based on Wavelet Neural Network under Missing Data

2015 ◽  
Vol 737 ◽  
pp. 76-80
Author(s):  
Jing Lu ◽  
Yan Qing Zhao ◽  
Yu Hong Zhao ◽  
Jun Yi Zhao ◽  
Chao Ying Yang
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Model Based ◽  
Wind Power Prediction ◽  
Mean Square Errors

Wind power prediction is a key problem in optimizing power dispatching. This paper builds a wind power prediction model based on wavelet neural network which substitutes wavelet basis function for the transfer function of hidden layer. A missing data interpolation strategy is also given to improve the applicability of the model. With the wind farm data from southeast coast, the model works and the wind power in the next 30 hours is predicted. In the sense of the mean square errors this paper compared the prediction results of the model and BP neural network model, the results shows the models have a better accuracy.

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