Abstract
We construct a class of elementary nonparametric output predictors of an unknown nonlinear system. Our algorithms predict asymptotically well for every bounded input sequence, every disturbance sequence in certain classes, and every nonlinear system that is bounded, continuous, and asymptotically time-invariant, causal, with decaying memory. The predictor uses only previous input and noisy output data of the system without any knowledge of the structure of the nonlinear system. Under additional smoothness conditions we provide rates of convergence for our scheme. Finally, we apply our results to the special case of stable LTI systems.
Original language | English (US) |
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Pages (from-to) | 4024-4029 |
Number of pages | 6 |
Journal | Proceedings of the IEEE Conference on Decision and Control |
Volume | 4 |
State | Published - 1995 |
Event | Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA Duration: Dec 13 1995 → Dec 15 1995 |
All Science Journal Classification (ASJC) codes
- Control and Optimization
- Control and Systems Engineering
- Modeling and Simulation