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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Mingyan Jiang
Shandong University
Computer Science & IT

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The New Complex-Valued Wavelet Neural Network Sufang Li; Mingyan Jiang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 3: September 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i3.95

Abstract

A new complex-valued wavelet neural network is proposed in this paper, by introducing a modified complex-valued back propagation algorithm, in which a new error function is to be minimized by the algorithm. The improvement performance is further confirmed by the simulation results, which show that the modified algorithm is simpler than the conventional algorithm, and has better convergence, better stability and faster running speed.
Hierarchical Real-time Network Traffic Classification Based on ECOC Yaou Zhao; Xiao Xie; Mingyan Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: February 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P) application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination port)look up table(TT-LUT) part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes) based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3877
An ICVBPNN Algorithm for Time-varying Channel Tracking and Prediction Sufang Li; Mingyan Jiang; Dongfeng Yuan
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5476-5483

Abstract

An improved complex-valued back propagation neural network (ICVBPNN) algorithm is proposed in this paper. In allusion to the defect of gradient descent of traditional complex-valued back propagation network (CVBPNN) algorithm, additive momentum has been introduced. It is used for time-varying channel tracking and prediction in wireless communication system and better application results are acquired. Firstly, with the use of the learning ability of the neural network, the tracking training is started based on the obtained channel state information (CSI), thus the nonlinear channel model is constructed. Secondly, the unknown channel state information is predicted using the ICVBPNN trained model. The simulation results demonstrate that the proposed method has less estimated error, and can track the channel more accurately than the traditional CVBPNN and the Kalman Filter algorithm.
Co-Authors Dongfeng Yuan Sufang Li Xiao Xie Yaou Zhao