Identification of linear systems: a practical guideline to accurate modeling
Identification of linear systems: a practical guideline to accurate modeling
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Multi-innovation stochastic gradient algorithm for output error systems based on the auxiliary model
ACC'09 Proceedings of the 2009 conference on American Control Conference
Parameter estimation for ARMAX systems using bias compensation methods
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Gradient based and least-squares based iterative identification methods for OE and OEMA systems
Digital Signal Processing
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This paper addresses the problem of parameter estimation of stochastic liner systems with noisy input-output measurements. A new and simple estimation scheme jor the variances of the white input and output measurement noises is presented, which is only based on expanding the denominator polynomial of the system transfer function and makes no use of the average least-squares errors. The attractive feature of the iterative least-square based parametric algorithm thus developed is its improved convergence property. The effectiveness of the developed identification algorithm is demonstrated through numerical illustrations.