Network traffic forecasting combination model based on wavelet transform and chaos algorithm

2014 ◽  
Vol 12 (03) ◽  
pp. 1450029 ◽  
Author(s):  
Xiao Chao Dang ◽  
Zhan Jun Hao ◽  
Yan Li ◽  
Zhen Yu Lu ◽  
Qi Gao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Traffic ◽  
Local Optimum ◽  
Elman Neural Network ◽  
Bp Network ◽  
Combination Model ◽  
Traffic Forecasting ◽  
Gradient Descent Algorithm ◽  
Chaos Algorithm

Based on wavelet transform and chaos algorithm, this paper presents a Network Traffic Forecasting Combination Model. The model introduces chaos algorithm for training the BP network and optimizing weights so as to avoid gradient descent algorithm that slowly converges and likely obtains local optimum results. Before forecasting, we first perform wavelet decomposition on the pretreated flow. Then, we utilize the FARIMA model and the improved Elman neural network model to forecast according to approximate components and detailed components, respectively. At last, we use the combination model for the network traffic forecasting. Simulation results confirmed the improved accuracy of the model, and comparing to traditional FARIMA model and wavelet neural network (WNN) model, the model can reduce the deviation.

Network Traffic Prediction Model of the New Elman Chaotic Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 2546-2554
Author(s):  
Xiao Chao Dang ◽  
Zhan Jun Hao ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Experimental Tests ◽  
Chaotic Neural Network ◽  
Seasonal Periodicity ◽  
Elman Neural Network ◽  
New Model ◽  
Network Training ◽  
Search Mechanism ◽  
New Algorithms

Considering such characteristics of the network system as nonlinearity, multivariate, and time variation, proposes a new improved Elman neural network model ------SIMF Elman. Seasonal periodicity learning methods are introduced into the learning process of the model. Chaotic search mechanism is introduced in the training process of the network weights. This new model uses the ergodicity of the Tent mapping to optimize the search of chaos variables, reducing data redundancy, and providing effective solution to the local convergence problem. Experimental tests of backbone network egress traffic of a certain university are conducted. The experimental results show that the new model and new algorithms can improve the network training speed and prediction accuracy of network traffic.

scholarly journals Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning

2021 ◽  
Author(s):  
Ning Li ◽  
Lang Hu ◽  
Zhong-Liang Deng ◽  
Tong Su ◽  
Jiang-Wang Liu
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Transfer Learning ◽  
Network Traffic ◽  
Prediction Algorithm ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Training Time ◽  
Gradient Descent Algorithm ◽  
Particle Filter Algorithm

AbstractIn this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.

scholarly journals Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3659
Author(s):  
Yiqi Liu ◽  
Longhua Yuan ◽  
Dong Li ◽  
Yan Li ◽  
Daoping Huang
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Oxygen Demand ◽  
Soft Sensor ◽  
Elman Network ◽  
Elman Neural Network ◽  
Dynamic Neural Network ◽  
Sensor Model ◽  
Marquardt Algorithm ◽  
Gradient Descent Algorithm

Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations. However, the traditional training methods of dynamic neural network may lead to poor local optima and low learning rates, resulting in inaccurate estimations of parameters and deviation of predictions. This study introduces a general Kalman-Elman method to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN). The method couples an Elman neural network with the square-root unscented Kalman filter (SR-UKF) to build a soft-sensor model. In the proposed methodology, adaptive noise estimation and weight constraining are introduced to estimate the unknown noise and constrain the parameter values. The main merits of the proposed approach include the following: First, improving the mapping accuracy of the model and overcoming the underprediction phenomena in data-driven process monitoring; second, implementing the parameter constraint and avoid large weight values; and finally, providing a new way to update the parameters online. The proposed method is verified from a dataset of the University of California database (UCI database). The obtained results show that the proposed soft-sensor model achieved better prediction performance with root mean square error (RMSE) being at least 50% better than the Elman network based on back propagation through the time algorithm (Elman-BPTT), Elman network based on momentum gradient descent algorithm (Elman-GDM), and Elman network based on Levenberg-Marquardt algorithm (Elman-LM). This method can give satisfying prediction of quality-related effluent variables with the largest correlation coefficient (R) for approximately 0.85 in output suspended solids (SS-S) and 0.95 in BOD and COD.

scholarly journals Prediction of PM2.5 concentration based on BP neural network optimized by bee colony algorithm

2022 ◽  
Vol 355 ◽  
pp. 03025
Author(s):  
Jie Heng ◽  
Min Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Local Optimum ◽  
Bp Network ◽  
Daily Average ◽  
Bee Colony ◽  
Neural Network Prediction ◽  
Bp Neural Network Model

According to the ambient air pollutants data and meteorological conditions data of Mianyang City in 2017, the BP neural network model based on MATLAB is established to predict the daily average PM2.5 concentration of Mianyang City in the next two days. However, the traditional BP network has the disadvantages of slow convergence speed and easy to fall into local optimum. In order to improve the prediction accuracy of the model, an optimization algorithm is added to the prediction model to avoid the model falling into local minimum. In this paper, the bee colony algorithm is added to the prediction model to improve the accuracy of BP neural network prediction model. The data from January to November are used for training, and the data from December are used as the verification results. The results show that the optimization model can accurately predict the daily average PM2.5 concentration of Mianyang City in the next two days, which provides a new idea for the prediction of PM2.5 concentration of the city, provides a theoretical basis for the early warning and decision-making of air pollution, and also provides more reliable prediction services for people’s daily travel.

A network traffic forecasting method based on SA optimized ARIMA–BP neural network

2021 ◽  
Vol 193 ◽  
pp. 108102
Author(s):  
Hanyu Yang ◽  
Xutao Li ◽  
Wenhao Qiang ◽  
Yuhan Zhao ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Traffic Forecasting ◽  
Forecasting Method
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