An interpretable and converging set-membership algorithm
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Induced ∞-norm FIR filter for recovering MPSK-type modulus signals
Signal Processing
Cournot games with linear regression expectations in oligopolistic markets
Mathematics and Computers in Simulation
Set-membership reduced-rank algorithms based on joint iterative optimization of adaptive filters
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Paper: Bounded-error parameter estimation: Noise models and recursive algorithms
Automatica (Journal of IFAC)
Brief Variable gain parameter estimation algorithms for fast tracking and smooth steady state
Automatica (Journal of IFAC)
Run-to-run control methods based on the DHOBE algorithm
Automatica (Journal of IFAC)
The size of the membership-set in a probabilistic framework
Automatica (Journal of IFAC)
Modified quasi-OBE algorithm with improved numerical properties
Signal Processing
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
Hi-index | 754.85 |
Continual updating of estimates required by most recursive estimation schemes often involves redundant usage of information and may result in system instabilities in the presence of bounded output disturbances. An algorithm which eliminates these difficulties is investigated. Based on a set theoretic assumption, the algorithm yields modified least-squares estimates with a forgetting factor. It updates the estimates selectively depending on whether the observed data contain sufficient information. The information evaluation required at each step involves very simple computations. In addition, the parameter estimates are shown to converge asymptotically, at an exponential rate, to a region around the true parameter.