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This study considers an adaptive neural-network (NN) periodic event-triggered control (PETC) problem for switched nonlinear systems (SNSs). In the system, only the system output is available at sampling instants. A novel adaptive law and a state observer are constructed by using only the sampled system output. A new output-feedback adaptive NN PETC strategy is developed to reduce the usage of communication resources; it includes a controller that only uses event-sampling information and an event-triggering mechanism (ETM) that is only intermittently monitored at sampling instants. The proposed adaptive NN PETC strategy does not need restrictions on nonlinear functions reported in some previous studies. It is proven that all states of the closed-loop system (CLS) are semiglobally uniformly ultimately bounded (SGUUB) under arbitrary switchings by choosing an allowable sampling period. Finally, the proposed scheme is applied to a continuous stirred tank reactor (CSTR) system and a numerical example to verify its effectiveness.Robotic grasping ability lags far behind human skills and poses a significant challenge in the robotics research area. According to the grasping part of an object, humans can select the appropriate grasping postures of their fingers. When humans grasp the same part of an object, different poses of the palm will cause them to select different grasping postures. Inspired by these human skills, in this article, we propose new grasping posture prediction networks (GPPNs) with multiple inputs, which acquire information from the object image and the palm pose of the dexterous hand to predict appropriate grasping postures. The GPPNs are further combined with grasping rectangle detection networks (GRDNs) to construct multilevel convolutional neural networks (ML-CNNs). In this study, a force-closure index was designed to analyze the grasping quality, and force-closure grasping postures were generated in the GraspIt! environment. Depth images of objects were captured in the Gazebo environment to construct the dataset for the GPPNs. Herein, we describe simulation experiments conducted in the GraspIt! environment, and present our study of the influences of the image input and the palm pose input on the GPPNs using a variable-controlling approach. In addition, the ML-CNNs were compared with the existing grasp detection methods. The simulation results verify that the ML-CNNs have a high grasping quality. The grasping experiments were implemented on the Shadow hand platform, and the results show that the ML-CNNs can accurately complete grasping of novel objects with good performance.This article studies the practical exponential stability of impulsive stochastic reaction-diffusion systems (ISRDSs) with delays. learn more First, a direct approach and the Lyapunov method are developed to investigate the pth moment practical exponential stability and estimate the convergence rate. Note that these two methods can also be used to discuss the exponential stability of systems in certain conditions. Then, the practical stability results are successfully applied to the impulsive reaction-diffusion stochastic Hopfield neural networks (IRDSHNNs) with delays. By the illustration of four numerical examples and their simulations, our results in this article are proven to be effective in dealing with the problem of practical exponential stability of ISRDSs with delays, and may be regarded as stabilization results.This article studies the rendezvous problem of linear multiagent systems by parallel event-triggered connectivity-preserving control strategies. There are two distinguished features of our design. First, the event-triggered control laws can not only guarantee the convergence of the tracking error as existing event-triggered consensus control strategies but also have the additional ability to maintain the connectivity of the time-varying and position-dependent communication network as rendezvous control laws. Second, by combining the potential function technique, output regulation theory, and adaptive control technique, an event-triggered observer is applied to estimate both the leader's system matrix and trajectory, which can work in parallel with the connectivity-preserving event-triggered controller. The executive time instants for the observer and the controller are asynchronous and generated by different triggering functions based on their own locally available measurement errors.This article presents a generalized collaborative representation-based classification (GCRC) framework, which includes many existing representation-based classification (RC) methods, such as collaborative RC (CRC) and sparse RC (SRC) as special cases. This article also advances the GCRC theory by exploring theoretical conditions on the general regularization matrix. A key drawback of CRC and SRC is that they fail to use the label information of training data and are essentially unsupervised in computing the representation vector. This largely compromises the discriminative ability of the learned representation vector and impedes the classification performance. Guided by the GCRC theory, we propose a novel RC method referred to as discriminative RC (DRC). The proposed DRC method has the following three desirable properties 1) discriminability DRC can leverage the label information of training data and is supervised in both representation and classification, thus improving the discriminative ability of the representation vector; 2) efficiency it has a closed-form solution and is efficient in computing the representation vector and performing classification; and 3) theory it also has theoretical guarantees for classification. Experimental results on benchmark databases demonstrate both the efficacy and efficiency of DRC for multiclass classification.In this article, a resilient H∞ approach is put forward to deal with the state estimation problem for a type of discrete-time delayed memristive neural networks (MNNs) subject to stochastic disturbances (SDs) and dynamic event-triggered mechanism (ETM). The dynamic ETM is utilized to mitigate unnecessary resource consumption occurring in the sensor-to-estimator communication channel. To guarantee resilience against possible realization errors, the estimator gain is permitted to undergo some norm-bounded parameter drifts. For the delayed MNNs, our aim is to devise an event-based resilient H∞ estimator that not only resists gain variations and SDs but also ensures the exponential mean-square stability of the resulting estimation error system with a guaranteed disturbance attenuation level. By resorting to the stochastic analysis technique, sufficient conditions are acquired for the expected estimator and, subsequently, estimator gains are obtained via figuring out a convex optimization problem. The validity of the H∞ estimator is finally shown via a numerical example.