In this paper a new neural network model for blind demixing of nonlinear rhixtures is proposed. We address the use of the Adaptive Spline Neural Network recently introduced for supervised and unsupervised neural networks. These networks are built using neurons with flexible B-spline activation bctions and in order to separate signals ffom mixtures, a gradient-ascending algorithm which maximize the outputs entropy is derived.These hnctions can change their shapes adapting few control points by the learning algorithm.Recently, under some control points constraints, the ASNNs have been successhlly applied using an unsupervised learning algorithm for linear BSS problems [3].As demonstrate in [3-51, the flexible activation hnctions used in the ASNNs have several interesting features: they 1) are easy to adapt, 2) have the necessary smoothing characteristics, 3) are easy to implement both in hardware and in software.
NON-LINEAR MIXING/DEMIXING BSSIn p&cular a suitable architecture composed by two layers of flexible nonlinear hnctions for the separation of nonlinear mixtures is proposed. Some experimental results that demonstrate the effectiveness of the MODEL proposed neural architecture are presented.
In this paper, a novel paradigm for blind source separation in the presence of nonlinear mixtures is presented. In particular, the paper addresses the problem of post-nonlinear mixing followed by another instantaneous mixing system. This model is called here the post-nonlinear-linear model. The method is based on the use of the recently introduced flexible activation function whose control points are adaptively changed: a neural model based on adaptive B-spline functions is employed. The signal separation is achieved through an information maximization criterion. Experimental results and comparison with existing solutions confirm the effectiveness of the proposed architecture.
In this paper a nonlinear deconvolving system, based on the use of the recently introduced flexible activation function whose control points are adaptively changed, is proposed. A specific learning algorithm for the proposed architecture, based on the information maximization techniques, is described. The monotonously increasing characteristic of the activation function is obtained using suitable B-spline functions imposing simple constraints on its control points.In order to test the effectiveness of this architecture some experiments, even in the presence of a strong nonlinear mixture, are presented and discussed.
In this paper a new adaptive non linear function for blind signal processing is presented. It is based on a spline approximation whose control points are adaptively changed using information maximization techniques. The monotonously increasing characteristic is obtained using suitable B-spline functions imposing simple constraints on its control points. In particular, the problem of adaptively maximizing the entropy of the output is considered for flattening (make uniform) the probability density function (pdf) of a random signal. We derive a simple form of the adaptation algorithm and present some experimental results that demonstrate the effectiveness of the proposed method.
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