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Both discrete and parametric faults are considered in our framework.
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For both discrete and rhythmic movement skills, we present a unified learning procedure for learning a parametric-attractor landscape from multiple demonstrations.
Both discrete and continuous models are proposed.
The model has both discrete and continuous features.
The resulting data were analyzed using both nonparametric and parametric linkage methods.
State estimation and fault diagnosis problems are discussed for a class of hybrid nonlinear systems modelled by hybrid automata, which have uncontrollable discrete mode transitions and parametric uncertainties.
The identification and estimation of both non-parametric and parametric SDP models is discussed briefly.
The daily returns were analyzed for randomness using both non-parametric and parametric tests.
For both parametric SRTM and parametric SRTM2, 40 basis functions were used.
Algebraic structures occur as both discrete examples and continuous examples.
In this paper, the robust stability problem of parametric uncertain QVNNs with both leakage, discrete and distributed delays has been investigated.
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