This article is devoted to data-driven optimal control of linear discrete systems with sensor fault via a performance triggering approach. A quadratic inequality is introduced to equivalently describe systems subject to a fault. A barrier function is provided by the input and output to treat the gain constraint. An optimal control law is built on the adaptive dynamic programming (ADP) method to improve control performance. A dynamic triggering mechanism is constructed by instantaneous data and performance index to balance triggering frequency and control performance, especially under an emergent situation. Sufficient conditions are supplied to ensure the ultimately uniform boundedness of the closed-loop systems. An illustrative example is presented to verify the validity of the proposed strategy.

This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

Aligning users across multiple heterogeneous social networks is a fundamental issue in many data mining applications. Methods that incorporate user attributes and network structure have received much attention. However, most of them suffer from error propagation or the noise from diverse neighbors in the network. To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label. Three major mechanisms in the model are designed to unitedly represent different attributes, distinguish different neighbors and capture the structure information of the ego networks respectively.

Contrast patterns describe differences between two or more data sets or data classes; they have been proven to be useful for solving many kinds of problems, such as building accurate classifiers, defining clustering quality measures, and analyzing disease subtypes. This article investigates the mining of a new kind of contrast patterns, namelydiscriminating inter‐attribute functions(DIFs), which represent arithmetic‐expression‐based inter‐attribute relationships that distinguish classes of data. DIFs are an expressive and practical alternative of item‐based contrast patterns and can express discriminating relationships such as “weight/(height)2is more likely to be ≤25 in one class than in another class.” Besides introducing the DIF mining problem, this article makes theoretical and algorithmic contributions on the problem. We prove that DIF mining is MAX SNP‐hard. Regarding how to efficiently mine DIFs, we present a set of rules to prune the search space of arithmetic expressions by eliminating redundant ones (equivalent to some others). We give two algorithms: one for finding all DIFs satisfying given thresholds and another for finding certain optimal DIFs using genetic computation techniques. The former is useful when the number of attributes is small, whereas the latter is useful when that number is large; both use the redundant arithmetic‐expression pruning rules. A performance study shows that our techniques are effective and efficient for finding DIFs.