4,801 research outputs found
On Using a Support Vector Machine in Learning Feed-Forward Control
For mechatronic motion systems, the performance increases significantly if, besides feedback control, also feed-forward control is used. This feed-forward part should contain the (stable part of the) inverse of the plant. This inverse is difficult to obtain if non-linear dynamics are present. To overcome this problem, learning feed-forward control can be applied. The properties of the learning mechanism are of importance in this setting. In the paper, a support vector machine is proposed as the learning mechanism. It is shown that this mechanism has several advantages over other learning techniques when applied to learning feed-forward control. The method is tested with simulation
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage
Aggregated functional data model for Near-Infrared Spectroscopy calibration and prediction
Calibration and prediction for NIR spectroscopy data are performed based on a
functional interpretation of the Beer-Lambert formula. Considering that, for
each chemical sample, the resulting spectrum is a continuous curve obtained as
the summation of overlapped absorption spectra from each analyte plus a
Gaussian error, we assume that each individual spectrum can be expanded as a
linear combination of B-splines basis. Calibration is then performed using two
procedures for estimating the individual analytes curves: basis smoothing and
smoothing splines. Prediction is done by minimizing the square error of
prediction. To assess the variance of the predicted values, we use a
leave-one-out jackknife technique. Departures from the standard error models
are discussed through a simulation study, in particular, how correlated errors
impact on the calibration step and consequently on the analytes' concentration
prediction. Finally, the performance of our methodology is demonstrated through
the analysis of two publicly available datasets.Comment: 27 pages, 7 figures, 7 table
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