The interpretability of quantum machine learning (QML) refers to the capability to provide clear and understandable explanations for the predictions and decision-making processes of QML models. However, most quantum convolutional layers (QCLs) utilize closed-box structures that are inherently devoid of interpretability, leading to the opacity of principles and the suboptimal mapping of classical data

This article addresses the problem of resilient collision avoidance distributed optimal coordination (DOC) for multiple Euler-Lagrangian (EL) systems under unreliable communication topologies. Due to adverse network conditions and cyber attacks, communication between agents can be disrupted during certain time intervals. To achieve collision avoidance between agents, a barrier function is redesigned

Recently, interest in flat-type projection clustering methods has grown as they improve learner's performance by exploring multiple projection subspaces. However, solvers used in previous representative works predominantly rely on greedy search strategies, which incur high computational costs and fail to consider interdependencies between projections. Moreover, these methods do not simultaneously guarantee

The randomness of chaotic systems are crucial for their application in secure communication. Conservative systems exhibit enhanced ergodicity and randomness in comparison to dissipative chaotic systems. However, the memristor-based conservative chaotic maps remain unreported. This article presents a study of volume-preserving chaotic maps based on discrete memristor (DM). We propose and analyze a generic

The artificial potential function (APF) is a widely applied algorithm in collision-free formation control in multiagent systems (MASs). However, it suffers from oscillations and acceleration surges, particularly when the current formation and the desired one conflict. To address this problem and enhance collision-free formation control in MAS, this article introduces the time-derivative of APFs. This

The competitive swarm optimizer (CSO) has been widely used for addressing multiobjective optimization problems owing to its diverse learning approach. However, the evolutionary process uncertainty within the algorithm weakens the optimization reliability. To deal with this concern, a robust multiobjective CSO with a predictive indicator (RMOCSO-PI), is proposed. This approach can reduce aimless and

This article presents a fixed-time vector-field-orientation (VFO) control law for unicycle-like nonholonomic mobile robots. We consider a set-point control problem in the presence of control inputs constraints and task-execution time constraints. The control law is based on the VFO methodology, which is characterized by nonoscillatory and well-predictable time evolution of transient states for unicycle-like

This article considers the design of prescribed-time sensor fault estimators (PSFEs) and unknown input compensation for linear systems, i.e., estimators that estimate the sensor fault and controllers that stabilize the state of the system both at a prescribed time. With this intention, a filter is introduced to eliminate the limitation of the fault being differentiable. Then, the task of designing