Radial basis function network using Lambert–Tsallis Wq function

Abstract

The present work brings two applications of the Lambert–Tsallis Wq 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 Wq function for q ∈{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.