Tracking characteristics of an OBE parameter-estimation algorithm

The tracking properties of a recursive parameter-bounding algorithm, referred to as the Dasgupta-Huang optimal bounding ellipsoid algorithm (DHOBE algorithm) are investigated. Conditions that ensure the existence of these 100% confidence regions in the face of small model-parameter variations are derived. For larger parameter variations, it is shown that the existence of 100% confidence regions is guaranteed asymptotically. A modification that enables the algorithm to track large variations in model parameter is proposed. Simulation results show that in general, the modified algorithm has a tracking performance comparable, and in some cases superior, to that of the exponentially weighted recursive-least-squares (RLS) algorithm. >

[1]  J. P. Norton,et al.  Parameter bounding from time-varying systems , 1990 .

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

[3]  E. Walter,et al.  Estimation of parameter bounds from bounded-error data: a survey , 1990 .

[4]  A. Benveniste,et al.  A measure of the tracking capability of recursive stochastic algorithms with constant gains , 1982 .

[5]  David D. Falconer,et al.  Tracking properties and steady-state performance of RLS adaptive filter algorithms , 1986, IEEE Trans. Acoust. Speech Signal Process..

[6]  Ashok K. Rao,et al.  Membership-Set Parameter Estimation via Optimal Bounding Ellipsoids , 1989 .

[7]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[8]  Bhaskar D. Rao,et al.  Tracking characteristics of the constrained IIR adaptive notch filter , 1987 .

[9]  J. Deller.  Set membership identification in digital signal processing , 1989, IEEE ASSP Magazine.

[10]  Yih-Fang Huang,et al.  ARMA parameter estimation using a novel recursive estimation algorithm with selective updating , 1990, IEEE Trans. Acoust. Speech Signal Process..

[11]  Lennart Ljung,et al.  Frequency domain tracking characteristics of adaptive algorithms , 1989, IEEE Trans. Acoust. Speech Signal Process..

[12]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[13]  S. Dasgupta,et al.  Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor , 1985 .