In this paper we revisit the design of neural-network based local linear models for dynamic system identification aiming at extending their use to scenarios contaminated with outliers. To this purpose, we modify well-known local linear models by replacing their original recursive rules with outlier-robust variants developed from the M-estimation framework. The performances of the proposed variants are evaluated in free simulation tasks over 3 benchmarking datasets. The obtained results corroborate the considerable improvement in the performance of the proposed models in the presence of outliers.
