Event-Triggered Distributed Cooperative Learning Algorithms over Networks via Wavelet Approximation

This paper investigates the problem of event-triggered distributed cooperative learning (DCL) over networks based on wavelet approximation theory, where each node only has access to local data which are produced by the same and unknown pattern (map or function). All nodes cooperatively learn this unknown pattern by exchanging learned information with their neighboring nodes under event-triggered strategy in order to remove unnecessary communications, so as to avoid the waste of network resources. For the above problem, two novel event-triggered continuous-time and discrete-time DCL algorithms are proposed to approximate the unknown pattern by using wavelet basis function. The proposed event-triggered DCL algorithms are used to train the optimal weight coefficient matrix of wavelet series. Moreover, the convergence of the proposed algorithms are presented by using the Lyapunov method, and the Zeno behavior is excluded as well by the strictly positive sampling interval. The illustrative examples are presented to show the efficiency and convergence of the proposed algorithms.

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