Efficient adaptive minimum variance control for discrete stochastic linear plant under unknown noise density: a NN-approach
Efficient adaptive minimum variance control for discrete stochastic linear plant under unknown noise density: a NN-approach
- Author(s):
- DOI: 10.1049/cp:19940250
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
International Conference on Control '94 — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): Source: International Conference on Control '94, 1994 p. 110 – 113
- Conference: International Conference on Control '94
- DOI: 10.1049/cp:19940250
- ISBN: 0 85296 610 5
- Location: Coventry, UK
- Conference date: 21-24 March 1994
- Format: PDF
We propose the recursive procedure for neural network approximation of the optimal transformation function using indirect adaptive control algorithm. The convergence and asymptotic normality theorems formulated above represent a theoretical basis for implementation of the adaptive version of the asymptotically efficient algorithm for the problem considered.
Inspec keywords: linear systems; noise; stochastic systems; feedforward neural nets; control system analysis; discrete systems; adaptive control
Subjects: Control system analysis and synthesis methods; Time-varying control systems; Neural nets (theory); Self-adjusting control systems; Discrete control systems
Related content
content/conferences/10.1049/cp_19940250
pub_keyword,iet_inspecKeyword,pub_concept
6
6