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This paper presents a fuzzy controller which can be designed through two steps for nonlinear systems.
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Suboptimal Girsanov's controls for estimating the time variant reliability are derived based on component level ideal controls, which are exactly obtainable for linear systems, and, via a local linearization step for nonlinear systems.
It is shown that a simplified version of the above closed loop controls, that avoids linearization step for nonlinear systems, can be deduced by minimizing a distance measure similar to what has been done for arriving at open loop controls.
We present in this paper the formulation of a new high-frequency dissipative time-stepping algorithm for nonlinear elastodynamics that is second-order accurate in time.
The combined scheme gives convergent solutions within the time step set for nonlinear stability of the corresponding nondissipative flow fields, and the time-dependent solutions are consistent with other model results using different methods.
The gain in efficiency by independently controlling the sub-process time steps is illustrated for nonlinear problems, where reaction and diffusion are much stiffer than advection.
Dimension reduction is an important preprocessing step for this nonlinear technique and is performed in a novel way: a genetic algorithm is designed which determines the optimal number of basis vectors for a reduced-order feature space representation as an optimization problem of the condition number of the resulting basis.
The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation.
In this paper, our aim is to develop a compensated split-step θ (CSSθ) method for nonlinear jump-diffusion systems.
We also confirm the mean-square stability of the split-step backward Euler method for nonlinear stochastic delay integro-differential equations.
In this section, we will discuss the mean-square stability of the split-step backward Euler method for nonlinear stochastic delay integro-differential equations.
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