A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Purification of entangled coherent states

We suggest an entanglement purification scheme for mixed entangled coherent states using 50-50 beam splitters and photodetectors. This scheme is directly applicable for mixed entangled coherent states of the Werner type, and can be useful for general mixed states using additional nonlinear interactions. We apply our scheme to entangled coherent states decohered in a vacuum environment and find the decay time until which they can be purified.