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In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law.
This article was published in the following journal.
Name: IEEE transactions on neural networks and learning systems
This paper studies an optimal consensus tracking problem of heterogeneous linear multiagent systems. By introducing tracking error dynamics, the optimal tracking problem is reformulated as finding a N...
This paper investigates the cooperative output regulation problem for heterogeneous linear multiagent systems under fixed communication graphs via event-triggered control. A fully distributed event-tr...
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Students' cooperative and prosocial behavior is vital to their social and academic success and to the quality of a school's social environment. This project will evaluate an instructional ...
Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health. Recent developmen...
The purpose of the study is to determine the validity of the prediction model in reducing the rate of CPAP titration failure and in achieving a shorter time to optimal pressure
Early term rehabilitation initiated after stroke should be based on motor learning principles and neural plasticity. To achieve motor learning and neural plasticity, exercises consisting o...
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Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
The neural systems which act on VASCULAR SMOOTH MUSCLE to control blood vessel diameter. The major neural control is through the sympathetic nervous system.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.