Identification and Prediction of Internet Traffic Using Artificial Neural Networks

Chabaa, Samira; Zeroual, Abdelouhab; Antari, Jilali

doi:10.4236/jilsa.2010.23018

Cited by 76 publications

(39 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The next sets of results to be discussed are those exhibited by a 2 hidden layer ANN with an architecture of (1,5,35,1). Once again the trend in Fig 4 indicate a decrease in generalization errors as the size of the training set is increased.…”

Section: Resultsmentioning

confidence: 99%

“…Inspired by biological systems, particularly the observation that biological learning systems are built of very complex webs of interconnected neurons, ANNs are able to learn and adapt from experience. They have demonstrated to be one of the most powerful tools in the domain of forecasting and analysis of various time series [1]. Time Series Forecasting (TSF) deals with the prediction of a chronologically ordered variable, and one of the most important application areas of TSF is in the domain of network engineering.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends

Moyo¹,

Sibanda²

2015

IJCA

View full text Add to dashboard Cite

In this paper we empirically investigate various sizes of training sets with the aim of determining the optimum training set size for generalization ability of an ANN trained on forecasting TCP/IP network traffic trends. We found from both the simulation experiments and literature that the best training set size can be obtained by selecting training samples randomly, between the interval 5 × and 10 × in number, depending on the difficulty of the problem under consideration. General TermsPattern Recognition.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends

Moyo¹,

Sibanda²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…NNs are being applied in predicting traffic in high-speed communication networks [15], and, traditionally, the model of choice is a feedforward NN (FNN) trained using a backpropagation algorithm [16]. FNNs are, however, not designed to handle dynamic systems and are therefore limited to handling stationary data.…”

Section: Methodsmentioning

confidence: 99%

A cooperative neural network approach for enhancing data traffic prediction

Abdulkarim¹,

Lawal²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

This paper addresses the problem of learning a regression model for the prediction of data traffic in a cellular network. We proposed a cooperative learning strategy that involves two Jordan recurrent neural networks (JNNs) trained using the firefly algorithm (FFA) and resilient backpropagation algorithm (Rprop), respectively. While the cooperative capability of the learning process ensures the effectiveness of the regression model, the recurrent nature of the neural networks allows the model to handle temporally evolving data. Experiments were carried out to evaluate the proposed approach using high-speed downlink packet access data demand and throughput measurements collected from different cell sites of a universal mobile telecommunications system-based cellular operator. The proposed model produced significantly superior results compared to the results obtained on the same problems from the traditional method of separately training a JNN with FFA and Rprop.

show abstract

“…Neural networks are widely used in the characterization of nonlinear systems [1][2][3][4][5][6][7], time-varying time-delay nonlinear systems [8] and they are applied in various applications [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor

Errachdi¹,

Saad²,

Benrejeb³

2012

JILSA

View full text Add to dashboard Cite

This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.

show abstract

Identification and Prediction of Internet Traffic Using Artificial Neural Networks

Cited by 76 publications

References 43 publications

Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends

Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends

A cooperative neural network approach for enhancing data traffic prediction

A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor

Contact Info

Product

Resources

About