Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

A recursive algorithm for updating the coefficients of a neural network structure for complex
signals is presented. Various complex activation functions are considered and a practical …

Neural networks (NNs) are able to give solutions to complex problems in digital
communications due to their nonlinear processing, parallel distributed architecture, self …

We investigate the approximation ability of a multi layer perceptron (MLP) network when it is
extended to the complex domain. The main challenge for processing complex data with …

Designing a neural network (NN) to process complex-valued signals is a challenging task
since a complex nonlinear activation function (AF) cannot be both analytic and bounded …

In this paper a new type of multilayer feedforward neural network is introduced. Such a
structure, called hypercomplex multilayer perceptron (HMLP), is developed in quaternion …

Among the useful blind equalization algorithms, stochastic-gradient iterative equalization
schemes are based on minimizing a nonconvex and nonlinear cost function. However, as …

Equalization of satellite communication using complex-bilinear recurrent neural network (C-
BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used …

In this paper, a new complex-valued neural network based on adaptive activation functions
is proposed. By varying the control points of a pair of Catmull-Rom cubic splines, which are …