Radial basis function network using Lambert-Tsallis Wq function

The present work brings two applications of the Lambert-Tsallis W-q function in radial basis function networks (RBFN). Initially, an RBFN is used to discriminate between entangled and disentangled bipartite of qubit states. The kernel used is based on the Lambert-Tsallis W-q function for q is an element of [1/2, 3/2, 2) and the quantum relative disentropy is used as a distance measure between quantum states. Entangled states with concurrence larger than 0.1 were correctly classified by the proposed RBFN in at least 97% of the cases. Following, a RBFN with the same kernel is used to estimate the probability density function of a set of data sampled according to Normal and Cauchy distributions. (C) 2019 Elsevier B.V. All rights reserved.