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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Sufang Li
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.
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 Mingyan Jiang