Distributed Event-Triggered Learning for Stochastic Configuration Networks
- Resource Type
- Conference
- Authors
- Ge, Xiaowen; Ai, Wu; Zhou, Yujun
- Source
- 2020 Chinese Automation Congress (CAC) Automation Congress (CAC), 2020 Chinese. :3384-3388 Nov, 2020
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Biological neural networks
Stochastic processes
Optimization
Task analysis
Symmetric matrices
Neurons
Event-triggered communication
distributed learning
stochastic configuration networks (SCNs)
distributed optimization
- Language
- ISSN
- 2688-0938
The purpose of this paper is to improve a distributed learning algorithm for stochastic configuration networks (SCNs) with event-triggered communication mechanism. During this process, the centralized SCNs problem will be converted to a distributed problem. The solution of this problem is the discrete-time zero-gradient-sum (ZGS) strategy. Then lead into the event-triggered communication mechanism to reduce the communication complexity of this distributed problem. With this mechanism, a trigger condition that whether the error exceeds the threshold or not determines whether or not each agent communicates. Information are transmitted between the agents only when they are in extraordinary need and the information is exchanged asynchronously. And based on ZGS strategy, we proposes a distributed learning algorithm. This algorithm can be used to train SCNs with event-triggered communication over networks. Finally, the output results of one simulation example are given to prove the validity of the proposed algorithm.