Stable on-line parameter identification with general knowledge on level of information noise discrete-time case
✍ Scribed by Fu-Ming Lee; Li-Chen Fu; I-Kong Fong
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 486 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0167-6911
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✦ Synopsis
An on-line parameter identification problem is posed and solved for discrete-time systems with general knowledge on the level of the inherent information noise. The knowledge can be the bound on either the magnitude or the finite-index ~P norm, pc[I, ~), of the noise. Based on the knowledge, a switching type gradient algorithm (or called gradient algorithm with dead zone) is proposed to estimate the parameters of the system from the available input-output data. In spite of the existence of the noise, this on-line algorithm guarantees that the estimation error is monotonically decreasing, and the parameter estimate is convergent to a steady-state value under a mild condition. Furthermore, the algorithm is stable in the sense that the estimation error will converge to zero as the bound on the noise gradually diminishes.