Paper Info
Title
Echo State Network With Multiple Loops Reservoir And Its Application In Network Traffic Prediction
Abstract
Echo state network (ESN), which w as proposed as a novel recurrent neural network (RNN), has already been proved to exhibit better prediction ability than traditional neural networks in dealing with time series prediction. However, ESN's randomly generated reservoir structure is of high complexity and can not guarantee the stability of the prediction. In this paper, we propose a novel ESN with deterministic multiple loops reservoir structure (MLR) to avoid the randomness of the reservoir in the classic ESN. In addition, compared with the adjacent-feedback loop reservoir structure (ALR), the novel reservoir structure strengthens the connection of neurons in the reservoir and improves the nonlinear approximation ability of ESN. To test its performance, our MLR-based ESN is applied to network traffic prediction. Extensive simulation results regarding to different prediction steps demonstrate that MLR can achieve higher prediction accuracy, and outperform existing prediction models. Furthermore, we also analyze the influence of parameters of MLR on the prediction accuracy, such as the neuronal interval of multiple loops and the number of loops.
Year
Venue
Keywords
2018
PROCEEDINGS OF THE 2018 IEEE 22ND INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN ((CSCWD))
Echo state networks, Parameter analysis, Network traffic prediction, Multiple loops reservoir
Field
DocType
Citations 
Time series,Computer science,Recurrent neural network,Algorithm,Prediction algorithms,Echo state network,Traffic prediction,Artificial neural network,Randomness,Nonlinear approximation,Distributed computing
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Lijuan Sun14713.26
Xinyan Yang200.34
Jian Zhou342.44
Juan Wang400.68
Fu Xiao511535.24