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We present a model for time series consisting of an infinite mixture of basis functions, whereby the bases and the mixing process are modelled as posterior means of latent Gaussian processes (GPs). Conditional to observed data, the bases and the mixing process are learnt using a parametric approximation based on pseudo-observations, where the complexity and accuracy of the method are controlled by...
In this paper efficient computer implementations of some of the most commonly used Volterra series based power amplifier behavioral models are proposed. The desired efficiency is in regard to algorithm complexity and floating point operations. Finally a comparative overview of the different behavioral models with respect to their complexity is presented.
The paper proposes the design and comparative study of two reduction methods of these models. The first, titled support vector regression (SVR) and the second is the projection method. Both methods use the Statistical Learning Theory (SLT) which operates on Reproducing Kernel Hilbert Space (RKHS) space. The performances of both methods are evaluated on the Tennessee Eastman process.
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