论文信息 - Parameter bounding from time-varying systems

Parameter bounding from time-varying systems

The computation of parameter bounds for models of linear, time-varying systems is considered. Three methods are discussed, all with counterparts in conventional least-squares or statistical parameter estimation: fixed-memory bounding, scalar bound inflation and bound incrementing. Their computational needs are examined, and some results from artificial and real records are presented.

J. P. Norton | S. H. Mo | J. Norton

[1] Yih-Fang Huang,et al. Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor , 1985, 1985 24th IEEE Conference on Decision and Control.

[2] J. P. Norton,et al. Identification and application of bounded-parameter models , 1985, Autom..

[3] J. Norton. Optimal smoothing in the identification of linear time-varying systems , 1975 .

[4] M. Milanese,et al. Estimation theory and uncertainty intervals evaluation in presence of unknown but bounded errors: Linear families of models and estimators , 1982 .

[5] F. Schweppe. Recursive state estimation: Unknown but bounded errors and system inputs , 1967 .

[6] Y. F. Huang,et al. On the value of information in system identification - Bounded noise case , 1982, Autom..

[7] J. P. Norton,et al. Fast and robust algorithm to compute exact polytope parameter bounds , 1990 .

[8] A. Jazwinski. Limited memory optimal filtering , 1968 .