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http://ir.lib.ncu.edu.tw/handle/987654321/51500
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Title: | Compact self-constructing recurrent fuzzy neural network with decision feedback for quadrature amplitude modulation signaling systems |
Authors: | Chang,YJ;Ho,CL |
Contributors: | 通訊工程學系 |
Keywords: | BACKPROPAGATION ALGORITHM;EQUALIZER |
Date: | 2011 |
Issue Date: | 2012-03-27 18:54:37 (UTC+8) |
Publisher: | 國立中央大學 |
Abstract: | This paper proposes a novel adaptive decision feedback equalizer (DFE) based on compact self-constructing recurrent fuzzy neural network (CSRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both compact self-constructing learning (CSL) and real-time recurrent learning algorithms is derived for the CSRFNN. The proposed CSL algorithm adopts two evaluation criteria to intelligently decide the number of fuzzy rules that are necessary. The real-time recurrent learning is performed simultaneously with the CSL at each time instant to adjust DFE parameters. The proposed DFE is compared with several neural network-based DFEs on a nonlinear complex-valued channel. The results show that the CSRFNN DFE is superior to classical neural network DFEs in terms of symbol-error rate, convergence speed, and time cost. Copyright (C) 2011 John Wiley & Sons, Ltd. |
Relation: | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING |
Appears in Collections: | [Department of Communication Engineering] journal & Dissertation
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