Abstract
A recurrent functional-link (RFL)-based cerebellar model articulation controller (CMAC) network used for solving the identification and prediction problem is proposed in this paper. The proposed RFL-based CMAC network has superior capability to the conventional CMAC network in efficient learning mechanism, guaranteed system stability and dynamic response. The network structure and its on-line learning algorithms for connective weights, means and standard derivations are described in detail. Finally, the RFL-based CMAC is applied in two computer simulations. Simulation results show that accurate identifying response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the proposed RFL-based CMAC network.
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