基于MEEMD-PE与CS-WNN模型的网络时延预测

doi:10.3969/j.issn.1001-506X.2020.01.25

Abstract

Abstract:

Aiming at the characteristics of randomness, instability and nonlinear induced delay of networked control system, a delay prediction algorithm of modified ensemble empirical mode decomposition-permutation entropy (MEEMD-PE) and wavelet neural network (WNN) optimized by cuckoo search (CS) is proposed. Firstly, MEEMD is used to decompose the original delay sequence, then the permutation entropy values of each sub sequence are calculated and recombination of new subsequences to reduce the non-stationary characteristics of the delay sequence and amount of calculation. And then predict the new subsequences by the WNN optimized by the CS algorithm. The final results of network delay are calculated by superimposing the prediction of the submodels. Simulation results show that the method has the advantages of better prediction accuracy, reflecting the overall trend of the delay sequence, and effectively reducing the impact of outliers.

Key words: networked control system, modified ensemble empirical mode decomposition (MEEMD), permutation entropy, cuckoo search (CS), wavelet neural network (WNN), delay prediction

CLC Number:

TP273

Weiguo SHI, Ming GUO. Network delay prediction based on model of modified ensemble empirical mode decomposition-permutation entropy and cuckoo search-wavelet neural network[J]. Systems Engineering and Electronics, 2020, 42(1): 184-190.

Figures/Tables 10

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References 26

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算法	完备性指标	整体正交性指标	分量数
EMD	4.996 3	0.217 3	7
MEEMD	2.397 9	0.063 2	9

新序列号	1	2	3	4	5
原模态序列号	1	2	3, 4	5, 6	7, 8, 9

预测方法	RMSE	MAPE	训练时间/s
本文方法	0.218	0.150	3.718
EMD-CS-WNN	1.507	1.366	5.045
EMD-LSSVM	2.382	1.903	10.981
CS-WNN	2.245	2.103	3.596
PSO-WNN	3.297	2.404	22.912
CS-BP	5.878	2.698	10.243

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Network delay prediction based on model of modified ensemble empirical mode decomposition-permutation entropy and cuckoo search-wavelet neural network

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