COMPLEX-VALUED RECURRENT NEURAL NETWORK WITH IIR NEURON MODEL: TRAINING AND APPLICATIONS

摘要

In this paper, based on the digital filter theory and approach, a new algorithm for training a complex-valued recurrent neural network, is proposed. Each recurrent neuron is modeled as an infinite impulse response (IIR) filter. The network weights are updated by optimizing the IIR filter coefficients, and the optimization is based on the layer-by-layer optimizing procedure as well as the recursive least-squares method. The performance of the proposed algorithm is demonstrated with application to a complex communication channel equalization. Our approach provides a new way to perform fast training of complex-valued recurrent neural networks.