Risk-sensitive and adaptive filtering by neural networks with long- and short-term memories

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 6, Issue , 1996, Pages 91-96

  Lo, James Ting Ho ^a  

^a UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; DIGITAL FILTERS; OPTIMIZATION; RISK ASSESSMENT; ROBUSTNESS (CONTROL SYSTEMS); SENSITIVITY ANALYSIS;

NONLINEAR FILTERING;

NEURAL NETWORKS;

EID: 0030308730     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (6)

1. K. Glover and J. C. Doyle. State-space formulae for all stabilizing controllers that satisfy an h-infinity norm bound and relations to risk-sensitivity. Systems Control Letters, 11:167-172, 1988.
2. B. Hassibi, A. H. Sayed, and T. Kailath. H-infinity optimality of the LMS algorithm. IEEE Transactions on Signal Processing, 44:267-280, 1996.
3. P. Huber. Robust Statistics. John Wiley and Sons, New York, 1981.
4. J. T. Lo. Neural network approach to optimal filtering. Invited paper presented at the 1992 World Congress of Nonlinear Analysts, Tampa, Florida, Aug. 1992.
5. J. T. Lo. Synthetic approach to optimal filtering. IEEE Transactions on Neural Networks, 5:803-811, September 1994.
6. J. L. Speyer, C.-H. Fan, and R. N. Banavar. Optimal stochastic estimation with exponential cost criteria. In Proceedings of the 31st Conference on Decision and Control, New York, New York, 1992. IEEE.