Robust neural training and pruning algorithms for a class of nonlinear tracking control systems

This thesis focuses on developing robust online training and pruning algorithms for a class of neural network tracking control systems. In particular, a complete convergence analysis is presented for all the algorithms with different learning schemes, respectively.

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Bibliographic Details
Main Author:	Ni, Jie
Other Authors:	Song Qing
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	https://hdl.handle.net/10356/4973
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Institution:	Nanyang Technological University

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Summary:	This thesis focuses on developing robust online training and pruning algorithms for a class of neural network tracking control systems. In particular, a complete convergence analysis is presented for all the algorithms with different learning schemes, respectively.

Robust neural training and pruning algorithms for a class of nonlinear tracking control systems

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