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Neural net robot controller: Structure and stability proofs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Robust control techniques for general dynamic systems
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Approximation by superpositions of a sigmoidal function
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Neural Networks for Control
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Adaptive filtering, prediction and control
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Multilayer feedforward networks are universal approximators
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Adaptive Control: the Model Reference Approach
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Robust control for the tracking of robot motion
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Neural network controller characteristics with regard to adaptive control
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Trajectory control of robotic manipulators using neural networks
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Identification and control of dynamical systems using neural networks
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A nonlinear regulator design in the presence of system uncertainties using multilayered neural network
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Adaptive manipulator control: A case study
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Stable adaptive control and recursive identification using radial Gaussian networks
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Neural networks as on-line approximators of nonlinear systems
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Adaptive Control Of Robot Manipulators
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Neural net robot controller with guaranteed tracking performance
inferredcites →https://doi.org/10.1109/tac.1987.1104543openalex-snapshot
A new adaptive law for robust adaptation without persistent excitation
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Adaptive control: The model reference approach
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Universal Approximation Using Radial-Basis-Function Networks
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On the Adaptive Control of Robot Manipulators
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Multilayer feedforward networks are universal approximators
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Adaptive pattern recognition and neural networks
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Adaptive Control Using Neural Networks
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A Summary Comparison of CMAC Neural Network and Traditional Adaptive Control Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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