121 research outputs found
Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks
Together with the Lyapunov-Krasovskii functional approach and an improved delay-partitioning idea, one novel sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic variable delay and both of delay variation limits can be measured. Through combining the reciprocal convex technique and convex technique one, the criterion is presented via LMIs and its solvability heavily depends on the sizes of both time-delay range and its variations, which can become much less conservative than those present ones by thinning the delay intervals. Finally, it can be demonstrated by four numerical examples that our idea reduces the conservatism more effectively than some earlier reported ones
Pinning Cluster Synchronization in Linear Hybrid Coupled Delayed Dynamical Networks
The problem on cluster synchronization will be investigated for a class of delayed dynamical networks based on pinning control strategy. Through utilizing the combined convex technique and Kronecker product, two sufficient conditions can be derived to ensure the desired synchronization when the designed feedback controller is employed to each cluster. Moreover, the inner coupling matrices are unnecessarily restricted to be diagonal and the controller design can be converted into solving a series of linear matrix inequalities (LMIs), which greatly improve the present methods. Finally, two numerical examples are provided to demonstrate the effectiveness and reduced conservatism
Design of Adaptive Switching Controller for Robotic Manipulators with Disturbance
Two adaptive switching control strategies are proposed for the trajectory tracking problem of robotic manipulator in this paper. The first scheme is designed for the supremum of the bounded disturbance for robot manipulator being known; while the supremum is not known, the second scheme is proposed. Each proposed scheme consists of an adaptive switching law and a PD controller. Based on the Lyapunov stability theorem, it is shown that two new schemes can guarantee tracking performance of the robotic manipulator and be adapted to the alternating unknown loads. Simulations for two-link robotic manipulator are carried out and show that the two schemes can avoid the overlarge input torque, and the feasibility and validity of the proposed control schemes are proved
Master-Slave Synchronization of Stochastic Neural Networks with Mixed Time-Varying Delays
This paper investigates the problem on master-salve synchronization for stochastic neural networks with both time-varying and distributed time-varying delays. Together with the drive-response concept, LMI approach, and generalized convex combination, one novel synchronization criterion is obtained in terms of LMIs and the condition heavily depends on the upper
and lower bounds of state delay and distributed one. Moreover, the addressed systems can include some famous network
models as its special cases, which means that our methods extend those present ones. Finally, two numerical examples are
given to demonstrate the effectiveness of the presented scheme
Contrastive Triple Extraction with Generative Transformer
Triple extraction is an essential task in information extraction for natural
language processing and knowledge graph construction. In this paper, we revisit
the end-to-end triple extraction task for sequence generation. Since generative
triple extraction may struggle to capture long-term dependencies and generate
unfaithful triples, we introduce a novel model, contrastive triple extraction
with a generative transformer. Specifically, we introduce a single shared
transformer module for encoder-decoder-based generation. To generate faithful
results, we propose a novel triplet contrastive training object. Moreover, we
introduce two mechanisms to further improve model performance (i.e., batch-wise
dynamic attention-masking and triple-wise calibration). Experimental results on
three datasets (i.e., NYT, WebNLG, and MIE) show that our approach achieves
better performance than that of baselines.Comment: Accepted by AAAI 202
Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks
Together with the Lyapunov-Krasovskii functional approach and an improved delay-partitioning idea, one novel sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic variable delay and both of delay variation limits can be measured. Through combining the reciprocal convex technique and convex technique one, the criterion is presented via LMIs and its solvability heavily depends on the sizes of both time-delay range and its variations, which can become much less conservative than those present ones by thinning the delay intervals. Finally, it can be demonstrated by four numerical examples that our idea reduces the conservatism more effectively than some earlier reported ones
Robust Control of Automotive Active Seat-Suspension System Subject to Actuator Saturation
This paper deals with the problem of robust sampled-data control for an automotive seatsuspension system subject to control input saturation. By using the nature of the sector nonlinearity, a sampled-data based control input saturation in the control design is studied. A passenger dynamic behavior is considered in the modeling of seat-suspension system, which makes the model more precisely and brings about uncertainties as well in the developed model. Robust output feedback control strategy is adopted since some state variables, such as, body acceleration and body deflection, are unavailable. The desired controller can be achieved by solving the corresponding linear matrix inequalities (LMIs). Finally, a design example has been given to demonstrate the effectiveness and advantages of the proposed controller design approach
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information
extraction to discover specific argument roles. In this study, we cast EAE as a
question-based cloze task and empirically analyze fixed discrete token template
performance. As generating human-annotated question templates is often
time-consuming and labor-intensive, we further propose a novel approach called
"Learning to Ask," which can learn optimized question templates for EAE without
human annotations. Experiments using the ACE-2005 dataset demonstrate that our
method based on optimized questions achieves state-of-the-art performance in
both the few-shot and supervised settings.Comment: work in progres
Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results
- …