The Design of an SCFNN Based Nonlinear Channel Equalizer

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.