A STOCHASTIC APPROACH TO LINEAR-ESTIMATION IN H-INFINITY

This paper examines the problem of system identification from frequenc y response data. Recent approaches to this problem, known collectively as 'Estimation in H-infinity', involve deterministic descriptions of noise corruptions to the data. In order to provide 'worst-case' conver gence with respect to these deterministic noise descriptions, non-line ar data algorithms are required. In contrast, this paper examines 'wor st-case' estimation in H-infinity when the disturbances are subject to mild stochastic assumptions and linearity in the data algorithms is e mployed. Issues of convergence, error bounds, and model order selectio n are considered. (C) 1998 Elsevier Science Ltd. All rights reserved.