A local learning algorithm for random weights networks

Robust modelling is significant to deal with complex systems with uncertainties. This paper aims to develop a novel learning algorithm for training regularized local random weights networks (RWNs). The learner model, terms as RL-RWN, is built on regularized moving least squares method and generalizes the solution obtained from the standard least square technique. Simulations are carried out using two benchmark datasets, including Auto-MPG data and surface reconstruction data. Results demonstrate that our proposed RL-RWN outperforms the original RWN and radial basis function networks.