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The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability.
We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments.
The experimental validation of proposed robust layered ensemble model included the test of the clustering procedure based on the Graded Possibilistic c-Means on an artificial data set with built-in concept drift and shift.
We consider the Diepenbeek/Hasselt set to be more representative of a typical car journey as it is a 15-km two-way travel from Diepenbeek to Hasselt and back, rather than an artificial data acquisition trajectory with the aim of covering as many streets and turns as possible in a short time and small area.
We test all implemented statistics on an artificial data set, somewhat similar to Tompa et al. [20], in order to control all variables.
We also demonstrate that the detector program used in both Ikegaya et al. (2004) and Mokeichev et al. (2007) is insufficiently sensitive to determine that an artificial data set, with many precise repeats implanted by the investigator, can be distinguished from surrogate data.
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Fig. 2 (a) Artificial data set (Banana set) divided into sequential groups for simulating a non-stationary domain.
Section 4 shows possible pitfalls with the aid of a simple artificial data set.
Hence, following the above methodology, we generate a large artificial data set by repeated resampling of the limited experimental repeats.
In this paper, the performance of the ILRBF-BP algorithm is compared with other well-known learning algorithms, such as back propagation based on stochastic gradient descent (SGBP) [29], the RBF algorithm based on k-means clustering (KM-RBF) [12], GAP-RBF, SVM, and an ELM, on artificial data sets.
We set up an experiment on artificial data to be able to compare the ranking after resampling to a known ground truth (see Methods section).
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