A new training scheme for neural networks and application in non-linear channel equalization

作者：Panda Sunita; Mohapatra Pradyumna K; Panigrahi Siba Prasada^*

来源：Applied Soft Computing, 2015, 27: 47-52.

DOI：10.1016/j.asoc.2014.10.040

• Artificial Neural Network
• Channel equalization
• Directed search optimization

This paper deals with the problem of equalization of channels in a digital communication system. In the literature, artificial neural network (ANN) has been increasingly used for the said problem. However, traditional methods of ANN training fall short of desired performance in the problem of equalization. In this paper, we propose a recently proposed training method for ANN for the problem. This training uses directed search optimization (DSO) as a trainer to neural networks. Then, we apply the same to the problem of nonlinear channel equalization and in that way, this paper introduces a novel strategy for equalization of nonlinear channels. Proposed method of channel equalization performs better than contemporary equalization methods used in the literature, as evident from extensive simulation results presented in this paper.

• 出版日期2015-2