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Our approach can be interpreted as approximate error linearization.
This paper presents an approach to the design of nonlinear observers by approximate error linearization.
Table 2 displays approximate error bounds for E k as defined by (5.5).
The approximate error of the new method converges much faster than the standard Monte Carlo method.
By optimizing an approximate error function and standard gradient based optimization algorithms, QARNN outputs conditional quantile functions recursively.
Using an analytical least-squares solution to the problem, a simple and approximate error analysis is developed.
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The H∞ design technique makes the controller robust to unmodeled dynamics, disturbances and approximate errors.
Then stability conditions and conservatism analysis are given, and the approximate errors are proven to be uniformly ultimately bounded (UUB).
The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control.
Table 1 Approximate errors and and the computed orders of convergence on the piecewise uniform mesh for various values of and.
The proposed method is robust to approximate errors, parameter mismatches and disturbances, because it integrates the merits of the adaptive fuzzy systems and the variable structure control with a sector.
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CEO of Professional Science Editing for Scientists @ prosciediting.com