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- W2754371570 abstract "This paper investigates an event-triggered distributed cooperative learning (DCL) algorithm using radial basis function networks (RBFNs), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over strongly connected and weight-balanced networks. The algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully distributed fashion. It has no requirement on central node and communication of raw data from each node during the training process by exchanging learned information with their neighboring nodes under event-triggered mechanism. The event-triggered DCL algorithm is used to train the optimal weight coefficient matrix of radial basis functions. The convergence of the proposed algorithm is presented by using the Lyapunov method, and the Zeno behavior is aovided. The illustrative example is presented to show the efficiency and fast convergence of the proposed algorithm." @default.
- W2754371570 created "2017-09-25" @default.
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- W2754371570 date "2017-07-01" @default.
- W2754371570 modified "2023-09-25" @default.
- W2754371570 title "Event-triggered distributed cooperative learning algorithm using radial basis function networks" @default.
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- W2754371570 doi "https://doi.org/10.23919/chicc.2017.8027992" @default.
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