Crouchgarrison2044

This article focuses on the H∞ adaptive tracking problem of uncertain switched systems. A key point of the study is to set up a multiple piecewise Lyapunov function framework which provides an effective tool for designing an adaptive switching controller consisting of a state-feedback and time-driven switching signal and a time-driven adaptive law. The proposed switching signal guarantees the solvability of the H∞ adaptive tracking problem for uncertain switched systems. Significantly, it provides plenty of adjusting time for the adaptive tracking control strategy to damp the transient caused by switching and avoids frequent switching. A novel time-driven adaptive switching controller is established such that the tracking error asymptotically converges to zero and all the signals in the error dynamic system are bounded under an achieved disturbance attenuation level. The solvability criterion ensuring an H∞ adaptive tracking performance is established for the uncertain switched systems, where the solvability of the H∞ adaptive tracking problem for individual subsystems is not required. Finally, the proposed method is applied to the electro-hydraulic unit.Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L-K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.This article presents a finite-time heterogeneous cyclic pursuit scheme that ensures consensus among agents modeled as integrators. It is shown that for the proposed consensus scheme, even when the gains are nonidentical, consensus results within a finite time, provided all the gains are positive or even if one gain is negative, subject to a lower bound. An algorithm is presented to compute the consensus value and consensus time for a given set of positive gains and initial states of the agents. The set of values, where consensus can occur, by varying the gains, has been derived and a second algorithm aids in determining the positive gains that enable consensus at any point in the aforementioned set, at a given finite time. As an application, the finite-time consensus in line-of-sight rates, over a cycle digraph, for a group of interceptors is shown to be effective in ensuring co-operative collision-free interception of a target, for both constant speed as well as realistic time-varying-speed models of the interceptors. Simulations validate the theoretical results.Determinants of user mental health are diverse, interrelated, and often multifaceted. This study explores how internet use, perceived care quality, patient education, and patient centered communication influence mental health, using structural equation modeling. Findings suggest that increased internet use even for health purposes negatively impacts mental health (= -0087; = -0065; P less then 0001). Dihydromyricetin in vivo On the other hand, education level, patient centered-communication (PC-Com) and perception of care quality impact mental health positively (= 0082; = 0146; = 0077; P less then 0001; respectively). Moreover, we also explored the changes across various demographics. The influence of patient education on PC-Com was only significant for Hispanic respondents (= -0160; P less then 0001). Internet use for health purposes influenced P C-Com negatively for White American respondents (= -0047; P = 0015). The study reinstated that the internet use, patient centered communication, patient education, and perceived care quality might influence mental health. The society will increasingly seek health information from online sources, so our study provides recommendations to make online health information sources more user friendly and trustworthy, ultimately to minimize negative impact on mental health.Twin support vector machine (TWSVM), which constructs two nonparallel classifying hyperplanes, is widely applied to various fields. However, TWSVM solves two quadratic programming problems (QPPs) separately such that the final classifiers lack consistency and enough prediction accuracy. Moreover, by reason of only considering the 1-norm penalty for slack variables, TWSVM is not well defined in the geometrical view. In this article, we propose a novel elastic net nonparallel hyperplane support vector machine (ENNHSVM), which adopts elastic net penalty for slack variables and constructs two nonparallel separating hyperplanes simultaneously. We further discuss the properties of ENNHSVM theoretically and derive the violation tolerance upper bound to better demonstrate the relative violations of training samples in the same class. In particular, we design a safe screening rule for ENNHSVM to speed up the calculations. We finally compare the performance of ENNHSVM on both synthetic datasets and benchmark datasets with the Lagrangian SVM, the twin parametric-margin SVM, the elastic net SVM, the TWSVM, and the nonparallel hyperplane SVM.This article studies the control problem for a class of stochastic nonlinear time-delay systems with uncertain output functions. Under the appropriate assumptions, a stabilization controller is explicitly constructed by applying the adding a power integrator method. Then, using the Lyapunov-Krasovskii functionals to address time-delay, it is proven that the designed controller can guarantee the closed-loop system to be globally asymptotically stable (GAS) in probability. Finally, two simulations show that the control strategy is effective and can be applied to the actual system.A swarming behavior problem is investigated in this article for heterogeneous uncertain agents with cooperation-competition interactions. In such a problem, the agents are described by second-order continuous systems with different intrinsic nonlinear terms, which satisfies the ``linearity-in-parameters condition, and the agents' models are coupled together through a distributed protocol containing the information of competitive neighbors. Then, for four different types of cooperation-competition networks, a distributed Lyapunov-based redesign approach is proposed for the heterogeneous uncertain agents, where the distributed controller and the estimation laws of unknown parameters are obtained. Under their joint actions, the heterogeneous uncertain multiagent system can achieve distributed stabilization for structurally unbalanced networks and output bipartite consensus for structurally balanced networks. In particular, the concept of coherent networks is proposed for structurally unbalanced directed networks, which is beneficial to the design of distributed controllers. Finally, four illustrative examples are given to show the effectiveness of the designed distributed controller.The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.Few-shot learning (FSL) aims to classify novel images based on a few labeled samples with the help of meta-knowledge. Most previous works address this problem based on the hypothesis that the training set and testing set are from the same domain, which is not realistic for some real-world applications. Thus, we extend FSL to domain-agnostic few-shot recognition, where the domain of the testing task is unknown. In domain-agnostic few-shot recognition, the model is optimized on data from one domain and evaluated on tasks from different domains. Previous methods for FSL mostly focus on learning general features or adapting to few-shot tasks effectively. They suffer from inappropriate features or complex adaptation in domain-agnostic few-shot recognition. In this brief, we propose meta-prototypical learning to address this problem. In particular, a meta-encoder is optimized to learn the general features. Different from the traditional prototypical learning, the meta encoder can effectively adapt to few-shot tasks from different domains by the traces of the few labeled examples. Experiments on many datasets demonstrate that meta-prototypical learning performs competitively on traditional few-shot tasks, and on few-shot tasks from different domains, meta-prototypical learning outperforms related methods.