Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm

Zhang, Weijie; Wei, Dengfeng

doi:10.1007/s00521-016-2483-5

Cited by 25 publications

(15 citation statements)

References 15 publications

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“…By adjusting the inertia weight and learning factor to improve the global search ability in the global extremum search, he was able to solve and avoid local optimal solution of PSO. Then he optimized the four parameters of the RBF so as to obtain the accuracy of the prediction model [22]. Lamentably, in the process of obtaining global optimal solutions, RBF has too many parameters to be optimized, and this will increase the calculation scale, the training time and affect the convergence rate.…”

Section: Pso-based Hybrid Modelmentioning

confidence: 99%

A comprehensive review on hybrid network traffic prediction model

Shi

Leau

et al. 2021

IJECE

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Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

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Section: Pso-based Hybrid Modelmentioning

confidence: 99%

A comprehensive review on hybrid network traffic prediction model

Shi

Leau

et al. 2021

IJECE

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show abstract

“…Then he optimized the four parameters of the model network so as to obtain the accuracy of the prediction model. However, it is easy to fall into the local optimal solution in the iteration process [15]. Table 2 summarizes the strengths and limitations of the optimization algorithms.…”

Section: Flow Chart Of Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…Where, ω is the random frequency, finish (ω − ω n )→ ω substitution, we can calculate the integral expression of the non-negative frequency interval, as shown in formula (15).…”

Section: ) Principle and Algorithm Process Of Variational Mode Decommentioning

confidence: 99%

Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion

Shi

Leau

et al. 2020

IEEE Access

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The 21st century is a high-tech information era in which our lives are closely linked by computer networks. Hence, how to effectively supervise networks and reduce the frequency of network security incidents has now become a research hotspot in cyberspace. Specifically, researchers have shown an increased interest in predicting the network traffic before any untoward incident happens. Optimization and decomposition technologies are the core components of network traffic prediction model which plays an important role in network management. This paper discusses past network traffic prediction research and critically examines the optimization and decomposition technologies used in the model, lists the model parameter structure based on the research methodology, the data set used, the evaluation criteria and so on. By comparison, digging out the Particle Swarm Optimization (PSO) algorithm and the Variational Mode Decomposition (VMD) decomposition technique will effectively solve the network traffic model predictive difficulties that have proven to be crucial to improving predictive accuracy and convergence speed strategy.The comprehensive review reveals that PSO and VMD are the most suitable optimization algorithm and decomposition technology for network traffic prediction modeling.

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“…A prediction method based on RBF neural network is put forward in [21] and an improved artificial bee colony algorithm is used to train the RBF neural network. Similarly, authors in [22] and [23] use improved gravitation search algorithm and particle swarm optimization algorithm respectively to optimize the RBF network for lower prediction error.…”

Section: Related Workmentioning

confidence: 99%

A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting

Zhang

You

2020

IEEE Access

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The accurate estimation of future network traffic is a key enabler for early warning of network degradation and automated orchestration of network resources. The long short-term memory neural network (LSTM) is a popular architecture for network traffic forecasting, and has been successfully used in many applications. However, it has been observed that LSTMs suffer from limited memory capacity problems when the sequence is long. In this paper, we propose a gated dilated causal convolution based encoder-decoder (GDCC-ED) model for network traffic forecasting. The GDCC-ED learns a vector representation in the encoder from historical network traffic series, in which gated dilated causal convolutions are adopted to expand the long-range memory capacity. Moreover, different types of features in various perspectives, including temporal-independent and temporal-related features, are incorporated. In the decoder, the GDCC-ED exploits an RNN with LSTM units to map the vector representation back to a variable-length target sequence. Besides, a sequence data augmentation technique is designed to solve the problem of data scarcity. Experimental results demonstrate that our model achieves superior performance than state-of-theart algorithms by 11.6%. INDEX TERMS Network traffic forecasting, dilated causal convolution, gated activations, encoder-decoder.

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Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm

Cited by 25 publications

References 15 publications

A comprehensive review on hybrid network traffic prediction model

A comprehensive review on hybrid network traffic prediction model

Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion

A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting

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