Abstract
In this paper a hybrid approach, based on a functional network and a neural network, for post-nonlinear independent component analysis is presented. In order to obtain the independence among the outputs, it was used as cost function a measure based on Renyi's quadratic entropy and Caudy-Schwartz inequality Also, the Kernel method was used for nonparametric estimation of the probability density function. The experimental results corroborated the soundness of the approach and a comparative study with a neural networks and its superior performance.
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