Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15755
Title: An optimal PID control algorithm for training feedforward neural networks
Authors: Jing, X 
Cheng, L 
Keywords: Feedforward neural networks
linear matrix inequality (LMI)
proportional integral and derivative (PID) controller
robust learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial electronics, 2013, v. 60, no. 6, 6185668, p. 2273-2283 How to cite?
Journal: IEEE transactions on industrial electronics 
Abstract: The training problem of feedforward neural networks (FNNs) is formulated into a proportional integral and derivative (PID) control problem of a linear discrete dynamic system in terms of the estimation error. The robust control approach greatly facilitates the analysis and design of robust learning algorithms for multiple-input-multiple-output (MIMO) FNNs using robust control methods. The drawbacks of some existing learning algorithms can therefore be revealed clearly, and an optimal robust PID-learning algorithm is developed. The optimal learning parameters can be found by utilizing linear matrix inequality optimization techniques. Theoretical analysis and examples including function approximation, system identification, exclusive-or (XOR) and encoder problems are provided to illustrate the results.
URI: http://hdl.handle.net/10397/15755
ISSN: 0278-0046
EISSN: 1557-9948
DOI: 10.1109/TIE.2012.2194973
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