Name
Title | ||
|---|---|---|
Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered Communication. |
Abstract | ||
|---|---|---|
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 condit... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2904253 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Artificial neural networks,Eigenvalues and eigenfunctions,Multi-agent systems,Symmetric matrices,Trajectory | Zeno's paradoxes,Neural control,Upper and lower bounds,Computer science,Multi-agent system,Event triggered,Artificial intelligence,Cooperative learning,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 2 | 2162-237X |
Citations | PageRank | References |
7 | 0.42 | 20 |
Authors | ||
5 | ||