2020
DOI: 10.1109/tie.2019.2950853
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Motor Learning and Generalization Using Broad Learning Adaptive Neural Control

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Cited by 135 publications
(54 citation statements)
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References 34 publications
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“…RBFNNs are a useful tool to approximate nonlinear functions for robot control and robot skills learning. For instance, RBFNNs is combined with the broad learning framework to learn and generalise the basic skills [29]. RBFNNs are employed to approximate the nonlinear dynamics of the manipulator robot to improve tracking performance [16,30].…”
Section: Radial Basis Function Neural Network (Rbfnns)mentioning
confidence: 99%
“…RBFNNs are a useful tool to approximate nonlinear functions for robot control and robot skills learning. For instance, RBFNNs is combined with the broad learning framework to learn and generalise the basic skills [29]. RBFNNs are employed to approximate the nonlinear dynamics of the manipulator robot to improve tracking performance [16,30].…”
Section: Radial Basis Function Neural Network (Rbfnns)mentioning
confidence: 99%
“…Compared with other classic structures, the efficiency and effectiveness of the BLS variants have been fully testified. What is more, Xu et al [32] raised a new adaptive neural control framework based on broad learning, giving birth to a better human neuromotor system than conventional adaptive neural control did. This is the initial attempt for the BLS to serve as an FBP classifier in FBP tasks.…”
Section: Broad Learning Systemmentioning
confidence: 99%
“…The energy parameters are set as E elec = 100 nJ/bit, ε fs = 10 pJ/bit/m 2 , and ε fs = 0.0013 pJ/bit/m 4 . (29,30)…”
Section: Energy Consumption Of Sensorsmentioning
confidence: 99%