Adaptive equalization of finite non-linear channels using multilayer perceptrons

Nonlinear Channel Equalization Using Multilayer Perceptrons with Information-theoretic Criterion

The minimum error entropy criterion was recently suggested in adaptive system training as an alternative to the mean-square-error criterion, and it was shown to produce better results in many tasks. In this paper, we apply a multiplayer perceptron scheme trained with this information theoretic criterion to the problem of nonlinear channel equalization. In our simulations, we use a realistic non...

Equalization of Non Linear Optical Channels

This paper presents a comparative analysis of various linear and nonlinear equalizers for a high density optical channel. Since the read-out process is nonlinear, a suitable model basedon the Volterra series has been adopted. In this work, we compare the performance and complexity of equalizers designed for linear and nonlinear channels. Specifically, Adaptive Minimum Mean Square Error (MSE), A...

A Linear Learning Method for Multilayer Perceptrons Using Least-Squares

Training multilayer neural networks is typically carried out using gradient descent techniques. Ever since the brilliant backpropagation (BP), the first gradient-based algorithm proposed by Rumelhart et al., novel training algorithms have appeared to become better several facets of the learning process for feed-forward neural networks. Learning speed is one of these. In this paper, a learning a...

Piecewise Linear Multilayer Perceptrons and Dropout

We propose a new type of hidden layer for a multilayer perceptron, and demonstrate that it obtains the best reported performance for an MLP on the MNIST dataset. 1 The piecewise linear activation function We propose to use a specific kind of piecewise linear function as the activation function for a multilayer perceptron. Specifically, suppose that the layer receives as input a vector x ∈ R. Th...

Model following using multilayer perceptrons