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This paper introduces a new generative semisupervised (transductive) mixture model with a more fine-grained class label generation mechanism than that of previous works. Our approach effectively combines the advantages of standard semisupervised mixtures, which achieve label extrapolation over a mixture component when there are few labeled samples, and nearest-neighbor (NN) classification, which achieves...
This paper presents an effective blind statistical identification technique for nonstationary nonlinear systems based on an information theoretical algorithm. This technique firstly extracts, from the output signals, the multivariate relationships in the Hilbert spaces by exploiting the separability properties of the signal outputs transformed by the Karhunen-Loeve transform (KLT). Then, the algorithm...
This paper presents model reference adaptive control (MRAC) approach to control the shape of output distribution in non-Gaussian stochastic systems. The method is based on Iterative Learning Control (ILC) and employs a neural network framework for controller design. The output probability density function (PDF) tracking problem is first reduced to dynamic neural network (NN) weight control. It is...
In this paper, a new method is proposed for predicting the future evolution of a time series of spacecraft telemetry data. Because such a time series has usually a non-stationary trend, nonlinear functional relationship between inputs and outputs and other uncertainties, an appropriate prediction model for spacecraft data is needed to set up. To this end, we analyze the characteristics of the Probability...
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