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.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING