In this paper, we apply a discrete-time learning algorithm to a class of discrete-time varying nonlinear systems with a$ne input action and linear output having relative degree one. We investigate the robustness of the algorithm to state disturbance, measurement noise and reinitialization errors. We
Perturbed Kuhn-Tucker points and rates of convergence for a class of nonlinear-programming algorithms
β Scribed by Stephen M. Robinson
- Publisher
- Springer-Verlag
- Year
- 1974
- Tongue
- English
- Weight
- 709 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0025-5610
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In this paper, for a Newton-like method for solving block nonlinear equations arising in the numerical solution of stiff ODEs y' = f(y), which involves a smaller quantity of computation, we prove that it is convergent and the convergence is independent of the stiffness of f(y), and give the error es
The authors regret that the above-referenced paper contains a number of misprints. In the statement of Theorem 3.1 (Eq. (3.1)) the condition C n+1 is incorrect. In fact, the set C n+1 in Theorem 3.1 should be replaced by the following one: The proof on page 15 line 5 should be inserted with the fol