Globally optimal bounding ellipsoid algorithm for parameter estimation using artificial neural networks

This paper develops a real-time implementation of a globally optimal bounding ellipsoid (GOBE) algorithm for parameter estimation of linear-in-parameter models with unknown but bounded (UBB) errors. A recently proposed recursively optimal bounding ellipsoid (ROBE) algorithm is introduced and a GOBE algorithm is derived through repeating this ROBE algorithm. An analogue artificial neural network (ANN) is provided to implement the GOBE algorithm in real time. Convergence analyses on the ROBE, the GOBE algorithms, and the analogue ANN implementation of the GOBE algorithm are presented. No persistent excitation condition is required to ensure the convergence. Simulation results show the good performances of these algorithms and the ANN implementation.