Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Feedforward neural networks training with optimal bounded ellipsoid algorithm
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
WSEAS Transactions on Computers
Recurrent neural networks training with stable bounding ellipsoid algorithm
IEEE Transactions on Neural Networks
Modified quasi-OBE algorithm with improved numerical properties
Signal Processing
Hi-index | 35.68 |
Optimal Bounding Ellipsoid (OBE) algorithms offer an attractive alternative to traditional least-squares methods for identification and filtering problems involving affine-in-parameters signal and system models. The benefits-including low computational efficiency, superior tracking ability, and selective updating that permits processor multi-tasking-are enhanced by multiweight (MW) optimization in which the data history is considered in determining update times and optimal weights on the observations. MW optimization for OBE algorithms is introduced, and an example MW-OBE algorithm implementation is developed around the recent quasi-OBE algorithm. Optimality of the solution is discussed, and simulation studies are used to illustrate performance benefits.