Please use this identifier to cite or link to this item:
Title: Methodology for neural network training for control of drives with nonlinearities
Authors: Low, Teck-Seng 
Lee, Tong-Heng 
Lim, Hock-Koon 
Issue Date: Apr-1993
Source: Low, Teck-Seng, Lee, Tong-Heng, Lim, Hock-Koon (1993-04). Methodology for neural network training for control of drives with nonlinearities. IEEE Transactions on Industrial Electronics 40 (2) : 243-249. ScholarBank@NUS Repository.
Abstract: The learning process of a multilayered feedforward neural network involves extracting a desired function from the training data presented through an appropriate training algorithm. To achieve the desired function, the generation of good training data is an important issue which needs to be addressed. This paper presents a closed-loop methodology for neural network training for control of drives with nonlinearities. In the paper problems associated with the more common open-loop training scheme, and how these are addressed by the proposed closed-loop method, are discussed. An inverse nonlinear control using neural network (INC/NN), a control strategy which incorporates the neural network for control of nonlinear systems, is described and used to demonstrate the effectiveness of the closed-loop training scheme for neural network. Simulation studies and experimental results are presented to verify the improvement achieved by the closed-loop training methodology.
Source Title: IEEE Transactions on Industrial Electronics
ISSN: 02780046
DOI: 10.1109/41.222646
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 7, 2018


checked on Mar 7, 2018

Page view(s)

checked on Feb 26, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.