This paper introduces a novel approach of Blind Separation in complex environment based on bi-dimensional flexible activation function (AF) and compares the performance of this architecture with the classical approach. The generalized complex function has been realized by a flexible bi-dimensional Spline Based approach both for the real and one for the imaginary parts, avoiding the restriction due to the Louiville’s theorem. The flexibility of the surface allows the learning of the control points using a gradient-based techniques. Some experimental results demonstrate the effectiveness of the proposed method
© 1991-2012 IEEE. A new blind signal separation (BSS) technique is proposed, enabling a deterministi...
AbstractA new algorithm of blind signal separation that jointly exploits the selection of rational n...
Blind Source Separation is one of the newest and most active research areas in adaptive filtering. I...
In this paper a natural gradient approach to blind source separation in complex environment is prese...
This paper proposes the blind separation of complex signals using a novel neural network architectur...
One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is ...
This chapter aims at introducing an Independent Component Analysis (ICA) approach to the separation ...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
In this paper, neural networks based on an adaptive nonlinear function suitable for both blind compl...
The problem of blind separation of complex-valued signals via joint diagonalization of a set of non-...
International audienceAbstract-This paper proposes a method of ''blind separation'' which extracts n...
This paper proposes a method of "blind separation" which extracts non-stationary signals (...
A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived ...
© 1991-2012 IEEE. A new blind signal separation (BSS) technique is proposed, enabling a deterministi...
AbstractA new algorithm of blind signal separation that jointly exploits the selection of rational n...
Blind Source Separation is one of the newest and most active research areas in adaptive filtering. I...
In this paper a natural gradient approach to blind source separation in complex environment is prese...
This paper proposes the blind separation of complex signals using a novel neural network architectur...
One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is ...
This chapter aims at introducing an Independent Component Analysis (ICA) approach to the separation ...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinea...
In this paper, neural networks based on an adaptive nonlinear function suitable for both blind compl...
The problem of blind separation of complex-valued signals via joint diagonalization of a set of non-...
International audienceAbstract-This paper proposes a method of ''blind separation'' which extracts n...
This paper proposes a method of "blind separation" which extracts non-stationary signals (...
A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived ...
© 1991-2012 IEEE. A new blind signal separation (BSS) technique is proposed, enabling a deterministi...
AbstractA new algorithm of blind signal separation that jointly exploits the selection of rational n...
Blind Source Separation is one of the newest and most active research areas in adaptive filtering. I...