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Benchmarks were random data sets as well as applications to coding and medical diagnosis problems.
Farms are visited to determine disease occurrence and to diagnosis problems.
The results prove that it represents an effective solution to fault diagnosis problems in Unmanned Aerial Vehicles.
This methodology is illustrated by the studies of diagnosis problems in the field of Chemical Process System Engineering.
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.
In this paper, we investigate the performance of global vs. local techniques applied to the training of neural network classifiers for solving medical diagnosis problems.
Similar(35)
The fault diagnosis problem is conceived as a classification problem.
Dynamic discontinuous high non-linear models characterize the diagnosis problem for batch processes.
In this paper, the actuator fault diagnosis problem for a class of bilinear systems with uncertainty is discussed.
In particular, networks trained by this learning rule are found to outperform standard backpropagation networks with novel patterns in the diagnosis problem.
Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension.
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