Utilizing the flexibility of disturbance soft-estimations with active elimination of the adverse effects caused by disturbances, a homogeneous domination feedback technique is integrated such that a general composite active anti-disturbance control framework is proposed.
First, we use the generalized adding a power integrator and the nested saturation methods to design a saturated homogeneous state feedback controller which globally finite-time stabilizes the nonlinear systems.
However, most previous works on collaborative recommendation only focus on exploiting homogeneous explicit feedbacks such as grade scores or homogeneous implicit feedbacks such as clicks or purchases.
However, most works only exploit users' homogeneous implicit feedbacks such as users' transaction records from "bought" activities, and ignore the other type of implicit feedbacks like examination records from "browsed" activities.
Specifically, we generalize Bayesian personalized ranking (BPR), a seminal pairwise learning algorithm for homogeneous implicit feedbacks, and learn the confidence adaptively, which is thus called adaptive Bayesian personalized ranking (ABPR).
The sufficient conditions for the existence of the global H∞-controllers are obtained via homogeneous-like state feedback and dynamic output feedback, respectively.
In 1952, Alan Turing suggested that spatial patterns could arise from homogeneous starting conditions by feedback amplification of stochastic fluctuations.
Specifically, we first design a homogeneous observer and an output feedback control law for each nominal subsystem without the nonlinearities.
More precisely, a passivity-enabling auxiliary system is proposed to provide necessary damping instead of velocity feedback and a homogeneous method is used to ensure finite-time convergence.
In reference [21], it was shown that the network can be pinned to a homogeneous state by applying adaptive feedback control actions to a small fraction of network nodes.
