Keywords: SCFNN, ANFIS, Bayesian solution, channel equalizer, neural network
Received June 30, 2004; revised November 29, 2004; accepted January 13, 2005.
Communicated by Liang-Gee Chen.
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Wan-De Weng1,2, Rui-Chang Lin1,3 and Chung-Ta Hsueh2
1Graduate School of Engineering Science and Technology
2Department of Electrical Engineering
National Yunlin University of Science and Technology
Yunlin, 640 Taiwan
E-mail: {wengwd, g9212718}@yuntech.edu.tw
3Department of Electronic Engineering
Nan Kai Institute of Technology
Nantou, 542 Taiwan
E-mail: rclin@nkc.edu.tw
The design of a self-constructing fuzzy neural network (SCFNN)-based digital
channel equalizer is proposed in this paper. We demonstrate that the SCFNN-based digital
channel equalizer possesses the ability to recover the channel distortion effectively.
The performance of SCFNN is compared with that of the adaptive-based-network fuzzy
inference system (ANFIS) and the optimal Bayesian solution. Simulations were carried
out in both real-valued and complex-valued nonlinear channels to demonstrate the flexibility
of the proposed equalizer. The experimental results show that the performance of
SCFNN can be close to that of the Bayesian optimal solution and ANFIS, while the
hardware requirement of the trained SCFNN-based equalizer is much lower.
Received June 30, 2004; revised November 29, 2004; accepted January 13, 2005.
Communicated by Liang-Gee Chen.