๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A system identification algorithm using orthogonal functions

โœ Scribed by Perez, H.; Tsujii, S.


Book ID
118690431
Publisher
IEEE
Year
1991
Tongue
English
Weight
366 KB
Volume
39
Category
Article
ISSN
1053-587X

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


System identification using discrete ort
โœ M. F. Fahmy; T. I. Haweei; G. J. M. Elraheem; R. R. Gharieb ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 577 KB

## Abstract In this paper a novel method is described for the design of adaptive IIR filters used in system identification. the adaptive filter is implemented as a parallel connection of subsections whose transfer functions constitute a set of discrete orthogonal systems. the adaptation algorithm u

System inversion using orthogonal functi
โœ F. L. Lewis; M. A. Christodoulou; B. G. Mertzios ๐Ÿ“‚ Article ๐Ÿ“… 1987 ๐Ÿ› Springer ๐ŸŒ English โš– 719 KB
Identification of time-varying linear sy
โœ S.G. Mouroutsos; P.N. Paraskevopoulos ๐Ÿ“‚ Article ๐Ÿ“… 1985 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 540 KB

This paper considers the problem of identifying the parameters and initial conditions of systems described by linear dtjierential equations with time-varying coefficients. A new approach is proposed which is based on the idea of using orthogonal functions to represent the input-output data, as well

A robust orthogonal algorithm for system
โœ M. J. Korenberg ๐Ÿ“‚ Article ๐Ÿ“… 1989 ๐Ÿ› Springer-Verlag ๐ŸŒ English โš– 902 KB

We describe and illustrate methods for obtaining a parsimonious sinusoidal series representation or model of biological time-series data. The methods are also used to identify nonlinear systems with unknown structure. A key aspect is a rapid search for significant terms to include in the model for t