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Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing...
A novel adaptive dynamic surface control (DSC) with recursive error surface is proposed for a class of uncertain nonlinear systems in strict-feedback form. The problem of explosion of terms in traditional backstepping design is eliminated by utilizing dynamic surface control. Furthermore, the maximum value of the norm of the ideal weighting vector in neural network (NN) systems is considered as the...
A novel adaptive dynamic surface control (DSC) with prescribed tracking performance is proposed for a class of uncertain nonlinear systems in pure-feedback form. By combining Nussbaum gain function (NGF) with decoupled backstepping, the problems of unknown control directions and control singularity are simultaneously solved. Furthermore, the norm of the ideal weighting vector in neural network (NN)...
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