On equalization with maximum covariance initialized cascade-correlation learning

In this paper we have studied the use of cascade-correlation (CC) trained multilayer perceptron (MLP) networks with a maximum covariance (MC) initialization scheme for adaptive equalization of binary data bursts in a baseband digital communication system. Conventional MLP networks have been found to perform well in various adaptive equalization tasks, although they often require quite a lot of computation. Here, we have used the CC training method for MLP networks in order to be able to create a nonlinear neural network equalizer with an adaptive structure. The CC training method finds a suitable sized network for each channel response and thus decreases the amount of computation needed. To further decrease the computational load of the system, we have applied the MC initialization scheme for weight initialization, which results in faster convergence and thus decreases the amount of training needed in the network