Robust neural controller for robot manipulator
67 p.
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2011
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sg-ntu-dr.10356-467512023-07-04T15:31:39Z Robust neural controller for robot manipulator Lou, Jia Ming Song Qing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics 67 p. Neural networks are widely used in industry fields, like robotic and process controllers. Extensive research and progress have also been done in various kinds of neural network algorithm, for example, the BP network, Hopfield network, RBF network, and a class of robust neural network algorithms. This thesis focuses on studying and developing robust online training and pruning algorithms for neural network tracking control systems. In particular, a complete convergence analysis is presented for Robust Adaptive Dead Zone training algorithm and Robust Adaptive Gradient Decent training algorithm, respectively. Master of Science (Computer Control and Automation) 2011-12-23T07:43:35Z 2011-12-23T07:43:35Z 2009 2009 Thesis http://hdl.handle.net/10356/46751 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Lou, Jia Ming Robust neural controller for robot manipulator |
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67 p. |
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Song Qing |
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Song Qing Lou, Jia Ming |
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Theses and Dissertations |
author |
Lou, Jia Ming |
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Lou, Jia Ming |
title |
Robust neural controller for robot manipulator |
title_short |
Robust neural controller for robot manipulator |
title_full |
Robust neural controller for robot manipulator |
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Robust neural controller for robot manipulator |
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Robust neural controller for robot manipulator |
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robust neural controller for robot manipulator |
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2011 |
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http://hdl.handle.net/10356/46751 |
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1772827118320746496 |