This article investigates the synthesis of distributed economic control algorithms under which dynamically coupled physical systems are regulated to a variational equilibrium of a constrained convex game. We study two complementary cases: 1) each subsy…
This article investigates the asynchronous distributed finite-time $H_{infty }$ filtering problem for nonlinear Markov jump systems over sensor networks under stochastic attacks. The stochastic attacks, called two-channel deception attacks, exist not…
In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or pr…
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the …
This article focuses on the sampled-data output-feedback control problem for nonlinear systems represented by Takagi–Sugeno fuzzy affine models. An input delay approach is adopted to describe the sample-and-hold behavior of the measurement outpu…
A new finite-time dynamic allocation and control scheme is developed in this article for multiple agents tracking a moving target. Based on a competitive manner, the dynamic allocation is achieved by k-winners-take-all (k-WTA), which can be realized by…
This article proposes a novel deep-learning framework, called RSSP, for real-time 3-D scene understanding with LiDAR sensors. To this end, we introduce new sparse strided operations based on the sparse tensor representation of point clouds. Compared wi…
Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight exper…
Probabilistic linear discriminant analysis (PLDA) is a very effective feature extraction approach and has obtained extensive and successful applications in supervised learning tasks. It employs the squared $L_{2}$ -norm to measure the model errors, wh…
This article is concerned with the event-triggered finite-time $H_{infty }$ estimator design for a class of discrete-time switched neural networks (SNNs) with mixed time delays and packet dropouts. To further reduce the data transmission, both the me…