This article proposes a $Q$ -learning (QL)-based algorithm for global consensus of saturated discrete-time multiagent systems (DTMASs) via output feedback. According to the low-gain feedback (LGF) theory, control inputs of the saturated DTMASs can avo…
As artificial systems are starting to be widely deployed in real-world settings, it becomes critical to provide them with the ability to discriminate between different informants and to learn from reliable sources. Moreover, equipping an artificial age…
Based on the distributed event-triggered impulsive mechanism, the leader-following mean-square consensus of stochastic multiagent systems with randomly occurring uncertainties and randomly occurring nonlinearities is investigated for the first time in …
Multiview learning has received substantial attention over the past decade due to its powerful capacity in integrating various types of information. Conventional unsupervised multiview dimension reduction (UMDR) methods are usually conducted in an offl…
High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimize…
In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization…
This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, …
In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quit…
This article discusses the adaptive fuzzy asymptotic tracking control for high-order nonlinear time-delay systems with full-state constraints. Fuzzy-logic systems and a separation principle are utilized to relax growth assumptions imposed on unknown no…
In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to le…