MLP-Based Equalization and Pre-Distortion Using an Artificial Immune Network

Due to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled.