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Coupled with marching-on-in-time (MOT) time domain integral equations, CNGTDA can facilitate efficient analysis of large scale time domain electromagnetic and acoustic problems.
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The problem of robust decentralized stabilization for a class of large-scale, time delay, and uncertain impulsive dynamical systems is introduced and studied.
The problem of adaptive fuzzy control for a class of large-scale, time-delayed systems with unknown nonlinear dead-zone is discussed here.
A decentralized adaptive neural output feedback control scheme is presented for a class of large-scale time-delay systems with saturating inputs.
We study the absolute stability of large-scale time-delay Lurie indirect control systems with unbounded coefficients.
In this paper, we deal with the absolute stability problem of a class of large-scale time-delay Lurie indirect control systems with unbounded coefficients.
Our combined framework thus improves and extends optical flow to the application of large-scale time-lapse fluorescence light microscopy images.
Further, in both, reinforcement signals (transmitted by climbing fibers in the cerebellum and dopaminergic nigrostriatal neurons in the basal ganglia) teach Purkinje (cerebellum) and striatal (basal ganglia) neurons to recognize "stamps" of large-scale, time-dependent neural activity by adjusting synaptic weights.
This simplified equation leads to the prediction of large scale space and time evolution of the energy quantities averaged over time, frequency and space.
Decentralized robust control problem is investigated for a class of large scale systems with time varying delays.
A very efficient and effective generalized Benders decomposition algorithm is then deployed, enabling the solution of large scale instances in reasonable time.
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